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Zou Z, Yang S, Zhao L. Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm. Sci Rep 2024; 14:19091. [PMID: 39154026 PMCID: PMC11330499 DOI: 10.1038/s41598-024-69911-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 08/09/2024] [Indexed: 08/19/2024] Open
Abstract
Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.
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Affiliation(s)
- Zuoming Zou
- Xi'an Jiaotong University, Xi'an, 710061, Shaanxi , China
| | - Shuming Yang
- Xi'an Jiaotong University, Xi'an, 710061, Shaanxi , China
| | - Liang Zhao
- School of Information Engineering, Yangzhou University, Yangzhou, 225009, Jiangsu, China.
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2
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Khalid A, Mushtaq Z, Arif S, Zeb K, Khan MA, Bakshi S. Control Schemes for Quadrotor UAV: Taxonomy and Survey. ACM COMPUTING SURVEYS 2024; 56:1-32. [DOI: 10.1145/3617652] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 08/21/2023] [Indexed: 08/25/2024]
Abstract
Quadrotor Unmanned Aerial Vehicle (UAV) is an unstable system, so it needs to be controlled efficiently and intelligently. Moreover, due to its non-linear, coupled, and under-actuated nature, the quadrotor has become an important research platform to study and validate various control theories. Different control approaches have been used to control the quadrotor UAV. In this context, a comprehensive study of different control schemes is presented in this research. First, an overview of the working and different applications of quadrotor UAVs is presented. Second, a mathematical model of the quadrotor is discussed. Later, the experimental results of various existing control techniques are discussed and compared. The various control schemes discussed and described for quadrotors are; Proportional Integral and Derivative (PID), Linear Quadratic Regulator (LQR), H-infinity (
H
∞
), Sliding Mode Control (SMC), Feedback Linearization (FBL), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Iterative Learning Control (ILC), Reinforcement Learning Control (RLC), Brain Emotional Learning Control (BELC), Memory Based Control (MBC), Nested Saturation Control (NSC), and Hybrid Controllers (HC). Comparison is done among all the control techniques and it is concluded that the hybrid control method gives improved results. This survey presents a broad overview of the state-of-the-art in UAV design, control, and implementation for real-life applications.
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Affiliation(s)
| | | | | | - Kamran Zeb
- National University of Sciences and Technology, Pakistan
| | - Muhammad Attique Khan
- HITEC University Taxila, Pakistan and Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
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3
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Zhao Z, Zhang J, Liu Z, Mu C, Hong KS. Adaptive Neural Network Control of an Uncertain 2-DOF Helicopter With Unknown Backlash-Like Hysteresis and Output Constraints. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:10018-10027. [PMID: 35439143 DOI: 10.1109/tnnls.2022.3163572] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.
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4
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Chen Z, Yan J, Ma B, Shi K, Yu Q, Yuan W. A Survey on Open-Source Simulation Platforms for Multi-Copter UAV Swarms. ROBOTICS 2023. [DOI: 10.3390/robotics12020053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
Abstract
Simulation platforms are critical and indispensable tools for application developments of unmanned aerial vehicles (UAVs) because the UAVs are generally costly, have certain requirements for the test environment, and need professional licensed operators. Thus, developers prefer (or have) to test their applications on simulation platforms before implementing them on real machines. In the past decades, a considerable number of simulation platforms for robots have been developed, which brings convenience to developers, but also makes them hard to choose a proper one as they are not always familiar with all the features of platforms. To alleviate this dilemma, this paper provides a survey of open-source simulation platforms and employs the simulation of a multi-copter UAV swarm as an example. The survey covers seven widely used simulators, including Webots, Gazebo, CoppeliaSim, ARGoS, MRDS, MORSE, and USARSim. The paper outlines the requirements for multi-copter UAV swarms and shows how to select an appropriate platform. Additionally, the paper presents a case study of a UAV swarm based on Webots. This research will be beneficial to researchers, developers, educators, and engineers who seek suitable simulation platforms for application development, (not only multi-copter UAV swarms but also other types of robots), which further helps them to save expenses for testing, and speed up development progress.
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Affiliation(s)
- Ziming Chen
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266400, China
| | - Jinjin Yan
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266400, China
| | - Bing Ma
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266400, China
| | - Kegong Shi
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266400, China
| | - Qiang Yu
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266400, China
| | - Weijie Yuan
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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5
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Li B, Bai S, Liang S, Ma R, Neretin E, Huang J. Manoeuvre decision‐making of unmanned aerial vehicles in air combat based on an expert actor‐based soft actor critic algorithm. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Affiliation(s)
- Bo Li
- School of Electronics and Information Northwestern Polytechnical University Xi'an China
| | - Shuangxia Bai
- School of Electronics and Information Northwestern Polytechnical University Xi'an China
| | - Shiyang Liang
- General department Avic Luoyang Electro‐optical Equipment Research Institute Luoyang China
| | - Rui Ma
- General department Xi'an Electronic Engineering Research Institute Xi'an China
| | - Evgeny Neretin
- School of Robotic and Intelligent Systems Moscow Aviation Institute Moscow Russia
| | - Jingyi Huang
- School of Electronics and Information Northwestern Polytechnical University Xi'an China
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6
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Li S, Sun Z. A generalized proportional integral observer–based robust tracking design approach for quadrotor unmanned aerial vehicle. INT J ADV ROBOT SYST 2022. [DOI: 10.1177/17298806221117052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
This article mainly studies the trajectory tracking control for quadrotor unmanned aerial vehicle with unknown time-varying disturbances including parametric uncertainties, model errors, and external disturbances such as wind effects. Conventional backstepping control schemes usually cannot guarantee the performance when it faces the time-varying disturbances. Improved schemes, such as integral backstepping, can only compensate the disturbances in a relatively slow way. By introducing disturbance observer technology into the design of controller, a composite generalized proportional integral observer–based robust control design method is developed. First, by utilizing the generalized proportional integral observer, the lumped time-varying disturbances of unmanned aerial vehicle are estimated. Secondly, combining the value of disturbance estimation and feedforward controller by using backstepping control technology together, a composite controller has been developed, which can be called as backstepping control + generalized proportional integral observer. The proposed control method has a better capability of disturbance rejection and is easy to implement. Simulation and experimental results illustrate the good robustness and tracking performance of the proposed scheme.
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Affiliation(s)
- Shenghui Li
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology Zijin college, Nanjing, China
| | - Zhenxing Sun
- School of Electronic and Control Science, Nanjing Tech University, Nanjing, China
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7
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System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs. SENSORS 2022; 22:s22134712. [PMID: 35808209 PMCID: PMC9269510 DOI: 10.3390/s22134712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/18/2022] [Indexed: 01/27/2023]
Abstract
Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller.
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8
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Fuzzy Based Backstepping Control Design for Stabilizing an Underactuated Quadrotor Craft under Unmodelled Dynamic Factors. ELECTRONICS 2022. [DOI: 10.3390/electronics11070999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Since the quadrotor unmanned aerial vehicle (UAV) is one of the systems that has four (4) control inputs and six (6) degree of freedom (DOF) which makes it as an underactuated system. Such underactuated mechatronic systems are very difficult to stabilize but at the same time these systems are power efficient and cost-effective because of a lower number of actuators. Later, if someone tries to stabilize this underactuated quadrotor UAV under the impact of unmodelled dynamic factors, it will lead to huge instability, low convergence rate, chattering effect, trajectory deviation and may also encounter some of the serious transient and steady state issues as well. This paper presents one of the adaptive-robust control algorithms, called the fuzzy based backstepping control (FBSC) design, to address the quadrotor’s helical trajectory tracking issue under an influence of unmodelled dynamic factors and external disturbances. This manuscript proposes the synthesis of the proposed FBSC design using MATLAB and Simulink software whereas these results are correlated with the conventional backstepping control (BSC) algorithm to show the effectiveness of the proposed algorithm by computing the integral absolute error values with and without disturbances.
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9
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Abstract
Conventional dynamic vibration absorbers are physical control devices designed to be coupled to flexible mechanical structures to be protected against undesirable forced vibrations. In this article, an approach to extend the capabilities of forced vibration suppression of the dynamic vibration absorbers into desired motion trajectory tracking control algorithms for a four-rotor unmanned aerial vehicle (UAV) is introduced. Nevertheless, additional physical control devices for mechanical vibration absorption are unnecessary in the proposed motion profile reference tracking control design perspective. A new dynamic control design approach for efficient tracking of desired motion profiles as well as for simultaneous active harmonic vibration absorption for a quadrotor helicopter is then proposed. In contrast to other control design methods, the presented motion tracking control scheme is based on the synthesis of multiple virtual (nonphysical) dynamic vibration absorbers. The mathematical structure of these physical mechanical devices, known as dynamic vibration absorbers, is properly exploited and extended for control synthesis for underactuated multiple-input multiple-output four-rotor nonlinear aerial dynamic systems. In this fashion, additional capabilities of active suppression of vibrating forces and torques can be achieved in specified motion directions on four-rotor helicopters. Moreover, since the dynamic vibration absorbers are designed to be virtual, these can be directly tuned for diverse operating conditions. In the present study, it is thus demonstrated that the mathematical structure of physical mechanical vibration absorbers can be extended for the design of active vibration control schemes for desired motion trajectory tracking tasks on four-rotor aerial vehicles subjected to adverse harmonic disturbances. The effectiveness of the presented novel design perspective of virtual dynamic vibration absorption schemes is proved by analytical and numerical results. Several operating case studies to stress the advantages to extend the undesirable vibration attenuation capabilities of the dynamic vibration absorbers into trajectory tracking control algorithms for nonlinear four-rotor helicopter systems are presented.
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10
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Yogi SC, Tripathi VK, Behera L. Adaptive Integral Sliding Mode Control Using Fully Connected Recurrent Neural Network for Position and Attitude Control of Quadrotor. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:5595-5609. [PMID: 33881998 DOI: 10.1109/tnnls.2021.3071020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor. A new fully connected recurrent neural network (FCRNN) controller has been proposed to mimic the equivalent control instead of estimating the Quadrotor dynamics separately. The proposed FCRNN architecture consists of output feedback to the input layer and the hidden layer, which enhances the approximation capability of FCRNN. All hidden layer neurons receive self-feedback and feedback from other hidden layer neurons, which further strengthens FCRNN's potential to capture complex dynamic characteristics. As learning should happen in finite time, the finite-time stability of the overall system has been guaranteed using the Lyapunov stability theory, and the update laws for FCRNN weights in real time are derived using the same. To show the effectiveness of the proposed approach, a comprehensive analysis has been done against existing SMC strategy and against well-known function approximation techniques, e.g., the radial basis function network (RBFN) and RNN.
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11
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Wang M, Chen B, Lin C. Prescribed finite-time adaptive neural trajectory tracking control of quadrotor via output feedback. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.06.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Robust Adaptive Backstepping Global Fast Dynamic Terminal Sliding Mode Controller Design for Quadrotors. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-021-01475-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV. ACTUATORS 2021. [DOI: 10.3390/act10080191] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The underactuated quadrotor unmanned aerial vehicle (UAV) is one of the nonlinear systems that have few actuators as compared to the degree of freedom (DOF); thus, it is a strenuous task to stabilize its attitude and positions. Moreover, an induction of unmodelled dynamic factors and uncertainties make it more difficult to control its maneuverability. In this paper, a model-free based single-dimension fuzzy sliding mode control (MFSDF-SMC) is proposed to control the attitude and positions of underactuated quadrotor UAV. The paper discusses the kinematic and dynamic models with unmodelled dynamic factors and unknown external disturbances. These unmodelled factors and disturbances may lead the quadrotor towards failure in tracking specific trajectory and may also generate some serious transient and steady-state issues. Furthermore, to avoid the problem of gimbal lock, the model is amalgamated with hyperbolic function to resolve the singularity issues dully developed due to Newton Euler’s dynamic modeling. The simulation results performed for MFSDF-SMC using MATLAB software R2020a are compared with conventional sliding mode control, fuzzy-based sliding control and single-dimension fuzzy-based sliding mode control without a model-free approach. The design and implementation of the model-free single dimension-based fuzzy sliding mode control (MFSDF-SMC) with an updated Lyapunov stability theorem is presented in this work. It is observed that MFSDF-SMC produces robust trajectory performance therefore, and the manuscript suggests the experimental setup to test the proposed algorithm in a noisy environment keeping the same conditions. The verification of the equipment used and its effective demonstration is also available for the reader within the manuscript.
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14
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Mehmood Y, Aslam J, Ullah N, Chowdhury MS, Techato K, Alzaed AN. Adaptive Robust Trajectory Tracking Control of Multiple Quad-Rotor UAVs with Parametric Uncertainties and Disturbances. SENSORS 2021; 21:s21072401. [PMID: 33807144 PMCID: PMC8036264 DOI: 10.3390/s21072401] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/18/2021] [Accepted: 03/25/2021] [Indexed: 11/19/2022]
Abstract
Recently, formation flying of multiple unmanned aerial vehicles (UAVs) found numerous applications in various areas such as surveillance, industrial automation and disaster management. The accuracy and reliability for performing group tasks by multiple UAVs is highly dependent on the applied control strategy. The formation and trajectories of multiple UAVs are governed by two separate controllers, namely formation and trajectory tracking controllers respectively. In presence of environmental effects, disturbances due to wind and parametric uncertainties, the controller design process is a challenging task. This article proposes a robust adaptive formation and trajectory tacking control of multiple quad-rotor UAVs using super twisting sliding mode control method. In the proposed design, Lyapunov function-based adaptive disturbance estimators are used to compensate for the effects of external disturbances and parametric uncertainties. The stability of the proposed controllers is guaranteed using Lyapunov theorems. Two variants of the control schemes, namely fixed gain super twisting SMC (STSMC) and adaptive super twisting SMC (ASTSMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, it is verified that in presence of disturbances, the proposed ASTSMC controller exhibits enhanced robustness as compared to the fixed gain STSMC.
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Affiliation(s)
- Yasir Mehmood
- School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan; (Y.M.); (J.A.)
| | - Jawad Aslam
- School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan; (Y.M.); (J.A.)
| | - Nasim Ullah
- Department of Electrical Engineering, College of Engineering Taif University, Al-Hawiyah, Taif P.O. Box 888, Saudi Arabia
- Correspondence:
| | - Md. Shahariar Chowdhury
- Faculty of Environmental Management, Prince of Songkla University, Hat Yai 90112, Thailand; (M.S.C.); (K.T.)
| | - Kuaanan Techato
- Faculty of Environmental Management, Prince of Songkla University, Hat Yai 90112, Thailand; (M.S.C.); (K.T.)
- Environmental Assessment and Technology for Hazardous Waste Management Research Center, Faculty of Environmental Management, Prince of Songkla University, Hat Yai 90112, Thailand
| | - Ali Nasser Alzaed
- Department of Architecture Engineering, College of Engineering Taif University, Al-Hawiyah, Taif P.O. Box 888, Saudi Arabia;
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15
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Zhang X, Wang Y, Zhu G, Chen X, Li Z, Wang C, Su CY. Compound Adaptive Fuzzy Quantized Control for Quadrotor and Its Experimental Verification. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:1121-1133. [PMID: 32413942 DOI: 10.1109/tcyb.2020.2987811] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article aims to realize a precise position and attitude tracking control for the quadrotor using a proposed fuzzy approximator-based compound adaptive fuzzy quantized control scheme. In the control scheme, a quantized output-feedback control for position tracking and a state-feedback quantized control for attitude trajectory tracking are combined to deal with the underactuated and strong coupling problems of the quadrotor. The main contributions are: 1) the adaptive fuzzy quantized control is realized, then the strong nonlinearities caused by the quantizer are effectively mitigated, which implies that the control precision can be improved when a low communication rate is required in the real-time control system of quadrotor; 2) by applying the adaptive fuzzy dynamic surface control (DSC) technique to the underactuated quadrotor control system, the "explosion of complexity" problem in the backstepping method is overcome and the L∞ tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated.
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16
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Affiliation(s)
- Ö. Bingöl
- Department of Electrical-Electronics Engineering, Gumushane University, Gumushane, Turkey
| | - H. M. Güzey
- Department of Electrical-Electronics Engineering, Erzurum Technical University, Erzurum, Turkey
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17
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Learning to Control a Quadcopter Qualitatively. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-020-01228-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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18
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Autonomous quadrotor collision avoidance and destination seeking in a GPS-denied environment. Auton Robots 2020. [DOI: 10.1007/s10514-020-09949-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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19
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Abstract
This paper presents a new control approach for the trajectory tracking of a quadrotor in the presence of external disturbances. Unlike in previous studies using hierarchical control strategies, a nonlinear controller is designed by introducing new state transformations that can use Euler angles as virtual control inputs. Thus, the proposed method can eliminate the timescale separation assumption of hierarchical control strategies. To estimate the external disturbances involved in the translational and rotational dynamics of the quadrotor, disturbance observers are developed. Using state transformations and estimates of external disturbances, we design a robust nonlinear controller based on the dynamic surface control method. The stability of the closed-loop system is analyzed without separation into two subsystems. From the Lyapunov stability theory, it is proven that all error signals in the closed-loop system are uniformly ultimately bounded and can be made arbitrarily small. Finally, simulation results are presented to demonstrate the performance of the proposed controller.
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20
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Yang Y, Liu Q, Qian Y, Yue D, Ding X. Secure bipartite tracking control of a class of nonlinear multi-agent systems with nonsymmetric input constraint against sensor attacks. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.05.086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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21
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Heterogeneous formation control of multiple UAVs with limited-input leader via reinforcement learning. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.040] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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22
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Mayorga-Macías WA, González-Jiménez LE, Meza-Aguilar MA, Luque-Vega LF. Velocity Sensor for Real-Time Backstepping Control of a Multirotor Considering Actuator Dynamics. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20154229. [PMID: 32751351 PMCID: PMC7435755 DOI: 10.3390/s20154229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/15/2020] [Accepted: 07/22/2020] [Indexed: 06/11/2023]
Abstract
A real-time implementation of a control scheme for a multirotor, based on angular velocity sensors for the actuators, is presented. The control scheme is composed of two loops: an inner loop for the actuators and an outer loop for the unmanned aerial vehicle (UAV). The UAV control algorithm is designed by means of the backstepping technique and a robust sliding mode differentiator, and the actuator control strategy is based on a standard proportional-integral-derivative (PID) controller. A robust exact differentiator, based on high order sliding modes, is used to estimate the complex derivatives present in the proposed control law. As the measurements of the propeller's angular velocities are required for the control law, velocity sensors are mounted in the axles of the rotors to retrieve them and a signal conditioning stage is implemented. In addition, dynamical models for the actuators of the aircraft were calculated by means of transfer functions obtained via experimental measurements in a test bench developed for this purpose. This test bench permits to characterize the parameters of the transfer functions by comparing the forces computed using the nominal parameter to the measured forces. To this end, it is assumed that the loads in the actuators of the vehicle are insignificant during flight. The effectiveness of the proposed sensor, its signal conditioning, and the overall control scheme are validated by means of simulation results and real-time experiments.
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Affiliation(s)
- Walter Alejandro Mayorga-Macías
- Department of Electronics Systems and Computing, Instituto Tecnológico y de Estudios Superiores de Occidente, ITESO AC, Tlaquepaque 45604, Jalisco, Mexico; (W.A.M.-M.); (M.A.M.-A.)
| | - Luis Enrique González-Jiménez
- Department of Electronics Systems and Computing, Instituto Tecnológico y de Estudios Superiores de Occidente, ITESO AC, Tlaquepaque 45604, Jalisco, Mexico; (W.A.M.-M.); (M.A.M.-A.)
| | - Marco Antonio Meza-Aguilar
- Department of Electronics Systems and Computing, Instituto Tecnológico y de Estudios Superiores de Occidente, ITESO AC, Tlaquepaque 45604, Jalisco, Mexico; (W.A.M.-M.); (M.A.M.-A.)
| | - Luis Fernando Luque-Vega
- Centro de Investigación, Innovación y Desarrollo Tecnológico CIIDETEC-UVM, Universidad del Valle de México, Tlaquepaque 45601, Jalisco, Mexico;
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Abstract
Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite of different controllers have been introduced in the literature for motion control of fixed and rotary wing vehicles, there are some challenges for improving controller features such as simplicity, robustness, efficiency, adaptability, and stability. This paper outlines a novel approach to deal with the induced effects of external disturbances affecting the flight of a quadrotor unmanned aerial vehicle. The aim of our study is to further extend the current knowledge of quadrotor motion control by using both adaptive and robust control strategies. A new adaptive neural trajectory tracking control strategy based on B-spline artificial neural networks and on-line disturbance estimation for a quadrotor is proposed. A linear extended state observer is used for estimating time-varying disturbances affecting the controlled nonlinear system dynamics. B-spline artificial neural networks are properly synthesized for on-line calculating control gains of an adaptive Proportional Integral Derivative (PID) scheme. Simulation results highlight the implementation of such a controller is able to reject disturbances meanwhile perform proper motion control by exploiting the robustness, disturbance rejection, adaptability, and self-learning capabilities.
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Pareto Optimal PID Tuning for Px4-Based Unmanned Aerial Vehicles by Using a Multi-Objective Particle Swarm Optimization Algorithm. AEROSPACE 2020. [DOI: 10.3390/aerospace7060071] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice. A selection of the PID controller parameters is required before the UAV can be used. Although there are guidelines for the design of PID parameters, they do not guarantee the stability of the UAV, which in many cases, leads to collisions involving the UAV during the calibration process. In this paper, an offline tuning procedure based on the multi-objective particle swarm optimization (MOPSO) algorithm for the attitude and altitude control of a Px4-based UAV is proposed. A Pareto dominance concept is used for the MOPSO to find values for the PID comparing parameters of step responses (overshoot, rise time and root-mean-square). Experimental results are provided to validate the proposed tuning procedure by using a quadrotor as a case study.
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Cervantes-Rojas JS, Muñoz F, Chairez I, González-Hernández I, Salazar S. Adaptive tracking control of an unmanned aerial system based on a dynamic neural-fuzzy disturbance estimator. ISA TRANSACTIONS 2020; 101:309-326. [PMID: 32143852 DOI: 10.1016/j.isatra.2020.02.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 02/07/2020] [Accepted: 02/09/2020] [Indexed: 06/10/2023]
Abstract
The main goal of this study is developing an adaptive controller which can solve the trajectory tracking for a class of quadcopter unmanned aerial system (UAS), namely a quadrotor. The control design introduces a new paradigm for adaptive controllers based on the implementation of a set of differential neural networks (DNNs) in the consequence section of a Takagi-Sugeno (T-S) fuzzy inference system. This dynamic fuzzy inference structure was used to approximate the UAS description. The particular form of interaction between neural networks and fuzzy inference systems proposed in the present work received the name of dynamic neural fuzzy system (DNFS). An adaptive controller based on this DNFS form was the main solution attained in this study. This DNFS controller was focused on the estimation and compensation of the uncertain section of the Quadrotor dynamics and then, forced the UAS to perform a hover flight while the tracking of desired angular positions succeeded, which results in tracking a desired trajectory in the X-Y plane. The control design methodology supported on the Lyapunov stability theory guaranteed ultimate boundedness of the estimation and tracking errors simultaneously. Several experimental tests in an outdoor environment by using a real Quadrotor platform was performed by using an RTK-GPS (Real Time Kinematic) system to determine the position of the vehicle in the X-Y plane. The experimental results confirmed the superior performance of the proposed algorithm based on the combination of DNNs and T-S techniques with respect to classical robust controllers.
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Affiliation(s)
- Jorge S Cervantes-Rojas
- Cátedras CONACYT, CITIS, AACyE, ICBI, Autonomous University of Hidalgo State, 42084 Pachuca, Hidalgo, Mexico.
| | - Filiberto Muñoz
- Cátedras CONACYT, UMI-LAFMIA, Cinvestav, 07360 Mexico City, Mexico.
| | - Isaac Chairez
- Bioprocesses Department, Interdisciplinary Professional Unit of Biotechnology, National Polytechnic Institute, 07340 Mexico City, Mexico.
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26
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The autonomous navigation and obstacle avoidance for USVs with ANOA deep reinforcement learning method. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105201] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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27
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Tang W, Wang L, Gu J, Gu Y. Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine. SENSORS 2020; 20:s20020345. [PMID: 31936223 PMCID: PMC7014280 DOI: 10.3390/s20020345] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 12/29/2019] [Accepted: 01/05/2020] [Indexed: 11/16/2022]
Abstract
The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.
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28
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Li J, Yang Q, Fan B, Sun Y. Robust State/Output-Feedback Control of Coaxial-Rotor MAVs Based on Adaptive NN Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:3547-3557. [PMID: 31095501 DOI: 10.1109/tnnls.2019.2911649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The coaxial-rotor micro-aerial vehicles (CRMAVs) have been proven to be a powerful tool in forming small and agile manned-unmanned hybrid applications. However, the operation of them is usually subject to unpredictable time-varying aerodynamic disturbances and model uncertainties. In this paper, an adaptive robust controller based on a neural network (NN) approach is proposed to reject such perturbations and track both the desired position and orientation trajectories. A complete dynamic model of a CRMAV is first constructed. When all system states are assumed to be available, an NN-based state-feedback controller is proposed through feedback linearization and Lyapunov analysis. Furthermore, to overcome the practical challenge that certain states are not measurable, a high-gain observer is introduced to estimate the unavailable states, and then, an output-feedback controller is developed. Rigorous theoretical analysis verifies the stability of the entire closed-loop system. In addition, extensive simulation studies are conducted to validate the feasibility of the proposed scheme.
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29
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Xi X, Liu T, Zhao J, Yan L. Output feedback fault-tolerant control for a class of nonlinear systems via dynamic gain and neural network. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04583-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Abstract
In this paper, by combining the dynamic gain and the self-adaptive neural network, an output feedback fault-tolerant control method was proposed for a class of nonlinear uncertain systems with actuator faults. First, the dynamic gain was introduced and the coordinate transformation of the state variables of the system was performed to design the corresponding state observers. Then, the observer-based output feedback controller was designed through the back-stepping method. The output feedback control method based on the dynamic gain can solve the adaptive fault-tolerant control problem when there are simple nonlinear functions with uncertain parameters in the system. For the more complex uncertain nonlinear functions in the system, in this paper, a single hidden layer neural network was used for compensation and the fault-tolerant control was realized by combining the dynamic gain. Finally, the height and posture control system of the unmanned aerial vehicle with actuator faults was taken as an example to verify the effectiveness of the proposed method.
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30
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Mohajerin N, Waslander SL. Multistep Prediction of Dynamic Systems With Recurrent Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:3370-3383. [PMID: 30714932 DOI: 10.1109/tnnls.2019.2891257] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this paper, we address the state initialization problem in recurrent neural networks (RNNs), which seeks proper values for the RNN initial states at the beginning of a prediction interval. The proposed methods employ various forms of neural networks (NNs) to generate proper initial state values for RNNs. A variety of RNNs are trained using the proposed NN initialization schemes for modeling two aerial vehicles, a helicopter and a quadrotor, from experimental data. It is shown that the RNN initialized by the NN-based initialization method outperforms the washout method which is commonly used to initialize RNNs. Furthermore, a comprehensive study of RNNs trained for multistep prediction of the two aerial vehicles is presented. The multistep prediction of the quadrotor is enhanced using a hybrid model, which combines a simplified physics-based motion model of the vehicle with RNNs. While the maximum translational and rotational velocities in the Quadrotor data set are about 4 m/s and 3.8 rad/s, respectively, the hybrid model produces predictions, over 1.9 s, which remain within 9 cm/s and 0.12 rad/s of the measured translational and rotational velocities, with 99% confidence on the test data set.
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31
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Active Fault-Tolerant Control of a Quadcopter against Time-Varying Actuator Faults and Saturations Using Sliding Mode Backstepping Approach. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9194010] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Fault-tolerant control is becoming an interesting topic because of its reliability and safety. This paper reports an active fault-tolerant control method for a quadcopter unmanned aerial vehicle (UAV) to handle actuator faults, disturbances, and input constraints. A robust fault diagnosis based on the H ∞ scheme was designed to estimate the magnitude of a time-varying fault in the presence of disturbances with unknown upper bounds. Once the fault estimation was complete, a fault-tolerant control scheme was proposed for the attitude system, using adaptive sliding mode backstepping control to accommodate the actuator faults, despite actuator saturation limitation and disturbances. The Lyapunov theory was applied to prove the robustness and stability of the closed-loop system under faulty operation. Simulation results show the effectiveness of the fault diagnosis scheme and proposed controller for handling actuator faults.
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32
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Zhou Z, Wang H, Hu Z, Wang Y, Wang H. A Multi-Time-Scale Finite Time Controller for the Quadrotor UAVs with Uncertainties. J INTELL ROBOT SYST 2019. [DOI: 10.1007/s10846-018-0837-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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33
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Koch W, Mancuso R, West R, Bestavros A. Reinforcement Learning for UAV Attitude Control. ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2019. [DOI: 10.1145/3301273] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using Proportional-Integral-Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However, more sophisticated control is required to operate in unpredictable and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Yet previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with state-of-the-art RL algorithms—Deep Deterministic Policy Gradient, Trust Region Policy Optimization, and Proximal Policy Optimization. To investigate these unknowns, we first developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then used our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.
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34
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Mitikiri Y, Mohseni K. Globally Stable Attitude Control of a Fixed-Wing Rudderless UAV Using Subspace Projection. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2895889] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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35
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Ma C, Wu W. Distributed synchronization of autonomous underwater vehicles with memorized protocol. INT J ADV ROBOT SYST 2019. [DOI: 10.1177/1729881419844149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
This article investigates the distributed synchronization problem of autonomous underwater vehicles by developing a novel synchronization protocol with memorized controller. More precisely, the memory information for information exchanges of autonomous underwater vehicles is utilized such that the synchronization performance can be improved. By employing the Lyapunov–Krasovskii functional method with model transformation, sufficient criteria are established for guaranteeing the synchronization, and the corresponding distributed synchronization controllers are designed based on matrix techniques. Finally, the effectiveness and benefits of our theoretical method are supported by an illustrative example with simulation results.
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Affiliation(s)
- Chao Ma
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wei Wu
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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36
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37
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Kang Y, Chen S, Wang X, Cao Y. Deep Convolutional Identifier for Dynamic Modeling and Adaptive Control of Unmanned Helicopter. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:524-538. [PMID: 29994727 DOI: 10.1109/tnnls.2018.2844173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Helicopters are complex high-order and time-varying nonlinear systems, strongly coupling with aerodynamic forces, engine dynamics, and other phenomena. Therefore, it is a great challenge to investigate system identification for dynamic modeling and adaptive control for helicopters. In this paper, we address the system identification problem as dynamic regression and propose to represent the uncertainties and the hidden states in the system dynamic model with a deep convolutional neural network. Particularly, the parameters of the network are directly learned from the real flight data of aerobatic helicopter. Since the deep convolutional model has a good performance for describing the dynamic behavior of the hidden states and uncertainties in the flight process, the proposed identifier manifests strong robustness and high accuracy, even for untrained aerobatic maneuvers. The effectiveness of the proposed method is verified by various experiments with the real-world flight data from the Stanford Autonomous Helicopter Project. Consequently, an adaptive flight control scheme including a deep convolutional identifier and a backstepping-based controller is presented. The stability of the flight control scheme is rigorously proved by the Lyapunov theory. It reveals that the tracking errors for both the position and attitude of unmanned helicopter asymptotic converge to a small neighborhood of the origin.
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38
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Fault-tolerant Control of Quadcopter UAVs Using Robust Adaptive Sliding Mode Approach. ENERGIES 2018. [DOI: 10.3390/en12010095] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this paper, a fault-tolerant control method is proposed for quadcopter unmanned aerial vehicles (UAV) to account for system uncertainties and actuator faults. A mathematical model of the quadcopter UAV is first introduced when faults occur in actuators. A normal adaptive sliding mode control (NASMC) approach is proposed as a baseline controller to handle the chattering problem and system uncertainties, which does not require information of the upper bound. To improve the performance of the NASMC scheme, radial basis function neural networks are combined with an adaptive scheme to make a quick compensation in presence of system uncertainties and actuator faults. The Lyapunov theory is applied to verify the stability of the proposed methods. The effectiveness of modified ASMC algorithm is compared with that of NASMC using numerical examples under different faulty conditions.
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39
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Output feedback tracking control of a class of continuous-time nonlinear systems via adaptive dynamic programming approach. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.07.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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40
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High-Order Sliding Mode-Based Fixed-Time Active Disturbance Rejection Control for Quadrotor Attitude System. ELECTRONICS 2018. [DOI: 10.3390/electronics7120357] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This article presents a fixed-time active disturbance rejection control approach for the attitude control problem of quadrotor unmanned aerial vehicle in the presence of dynamic wind, mass eccentricity and an actuator fault. The control scheme applies the feedback linearization technique and enhances the performance of the traditional active disturbance rejection control (ADRC) based on the fixed-time high-order sliding mode method. A switching-type uniformly convergent differentiator is used to improve the extended state observer for estimating and attenuating the lumped disturbance more accurately. A multivariable high-order sliding mode feedback law is derived to achieve fixed time convergence. The timely convergence of the designed extended state observer and the feedback law is proved theoretically. Mathematical simulations with detailed actuator models and real time experiments are performed to demonstrate the robustness and practicability of the proposed control scheme.
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41
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Modified Consensus-based Output Feedback Control of Quadrotor UAV Formations Using Neural Networks. J INTELL ROBOT SYST 2018. [DOI: 10.1007/s10846-018-0961-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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42
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Li Z, Ma X, Li Y. Model-free control of a quadrotor using adaptive proportional derivative-sliding mode control and robust integral of the signum of the error. INT J ADV ROBOT SYST 2018. [DOI: 10.1177/1729881418800885] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this article, a robust model-free trajectory tracking control strategy is developed for a quadrotor in the presence of external disturbances. The proposed strategy has an outer-inner-loop control structure. The outer loop controls the position with adaptive proportional derivative-sliding mode control and generates the desired attitude angles for the inner loop corresponding to the given position, velocity, and heading references, while the robust integral of the signum of the error method is applied to the inner loop to guarantee fast convergence of attitude angles. Asymptotic tracking of the three-dimensional trajectories is proven by the Lyapunov stability theory. The effectiveness of the proposed controller is demonstrated with the simulation results by comparing with other model-free quadrotor trajectory tracking controllers.
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Affiliation(s)
- Zhi Li
- Center for Robotics, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Xin Ma
- Center for Robotics, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Yibin Li
- Center for Robotics, School of Control Science and Engineering, Shandong University, Jinan, China
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43
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Affiliation(s)
- S. Suzuki
- Department of Mechanical Engineering and Robotics, Shinshu University, Ueda-shi, Nagano, Japan
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44
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Liu Z, Zhang Y, Yuan C, Ciarletta L, Theilliol D. Collision Avoidance and Path Following Control of Unmanned Aerial Vehicle in Hazardous Environment. J INTELL ROBOT SYST 2018. [DOI: 10.1007/s10846-018-0929-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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45
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Zeghlache S, Mekki H, Bouguerra A, Djerioui A. Actuator fault tolerant control using adaptive RBFNN fuzzy sliding mode controller for coaxial octorotor UAV. ISA TRANSACTIONS 2018; 80:267-278. [PMID: 29885739 DOI: 10.1016/j.isatra.2018.06.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 03/24/2018] [Accepted: 06/01/2018] [Indexed: 06/08/2023]
Abstract
In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial octorotor helicopter control is proposed in presence of actuator faults. Radial Base Function Neural Network (RBFNN), Fuzzy Logic Control approach (FLC) and Sliding Mode Control (SMC) technique are used to design a controller, named Fault Tolerant Control (FTC), for each subsystem of the octorotor helicopter. The proposed FTC scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller, and guaranteeing the stability and the robustness of the system. The simulation results show that the proposed FTC can greatly alleviate the chattering effect, good tracking in presence of actuator faults.
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Affiliation(s)
- Samir Zeghlache
- LASS, Laboratoire d'Analyse des Signaux et Systèmes, Department of Electrical Engineering, Faculty of Technology, Mohamed Boudiaf University of Msila, BP 166 Ichbilia, Msila, Algeria.
| | - Hemza Mekki
- LGE, Laboratoire de Génie Electrique, Department of Electrical Engineering, Faculty of Technology, Mohamed Boudiaf University of Msila, BP 166 Ichbilia, Msila, Algeria.
| | - Abderrahmen Bouguerra
- LGE, Laboratoire de Génie Electrique, Department of Electrical Engineering, Faculty of Technology, Mohamed Boudiaf University of Msila, BP 166 Ichbilia, Msila, Algeria.
| | - Ali Djerioui
- LGE, Laboratoire de Génie Electrique, Department of Electrical Engineering, Faculty of Technology, Mohamed Boudiaf University of Msila, BP 166 Ichbilia, Msila, Algeria.
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46
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Sun M, Liu J, Wang H, Nian X, Xiong H. Robust fuzzy tracking control of a quad-rotor unmanned aerial vehicle based on sector linearization and interval matrix approaches. ISA TRANSACTIONS 2018; 80:336-349. [PMID: 30093101 DOI: 10.1016/j.isatra.2018.07.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 01/16/2018] [Accepted: 07/24/2018] [Indexed: 06/08/2023]
Abstract
In this paper, the robust H∞ fuzzy tracking control strategy for a quad-rotor unmanned aerial vehicle (UAV) with strong coupling and highly nonlinear is put forward based on the Takagi-Sugeno(T-S) fuzzy error model. Firstly, the quad-rotor UAV system is divided into altitude subsystem, position subsystem and attitude subsystem. Through selecting appropriate premise variables, three T-S fuzzy error models, which are equivalent to the error dynamic model, are established by the sector linearization approach. Next, the uncertainties in drag coefficients, moments of inertia are taken into account, and the interval matrix is introduced to describe them in altitude, position and attitude T-S fuzzy error models. Then the robust H∞ fuzzy feedback controllers are designed to stabilize T-S fuzzy subsystems. Besides, according to the Lyapunov stability theorem, it is obtained that the LMI sufficient conditions of exponential stability with the prescribed H∞ performance for T-S fuzzy closed-loop subsystems. Meanwhile, the method for solving the gain matrices of controller is presented. Finally, simulation results are given to demonstrate the effectiveness, robustness and advantages of the proposed method. Then the feasibility of the proposed method is further verified by experimental results.
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Affiliation(s)
- Miaoping Sun
- School of Information Science and Engineering, Central South University, Changsha, Hunan 410004, PR China.
| | - Jingjing Liu
- School of Information Science and Engineering, Central South University, Changsha, Hunan 410004, PR China
| | - Haibo Wang
- School of Information Science and Engineering, Central South University, Changsha, Hunan 410004, PR China
| | - Xiaohong Nian
- School of Information Science and Engineering, Central South University, Changsha, Hunan 410004, PR China
| | - Hongyun Xiong
- School of Information Science and Engineering, Central South University, Changsha, Hunan 410004, PR China
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47
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Boudjit K, Larbes C, Ramzan N. ANN design and implementation for real-time object tracking using quadrotor AR.Drone 2.0. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1509896] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Kamel Boudjit
- Electronics Department, Ecole Nationale Polytechnique, ENP, Algiers, Algeria
| | - Cherif Larbes
- Electronics Department, Ecole Nationale Polytechnique, ENP, Algiers, Algeria
| | - Naeem Ramzan
- Electronics Department, University of the West of Scotland UWS, Paisley, UK
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48
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Szanto N, Narayanan V, Jagannathan S. Event-Sampled Direct Adaptive NN Output- and State-Feedback Control of Uncertain Strict-Feedback System. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:1850-1863. [PMID: 28422691 DOI: 10.1109/tnnls.2017.2678922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this paper, a novel event-triggered implementation of a tracking controller for an uncertain strict-feedback system is presented. Neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be unknown and an NN observer is used to estimate the state vector. By using the estimated state vector and backstepping design approach, an event-sampled controller is introduced. As part of the controller design, first, input-to-state-like stability for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated, and subsequently, an event-execution control law is derived, such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors, the observer estimation errors, and the NN weight estimation errors for each NN are locally uniformly ultimately bounded in the presence bounded disturbances, NN reconstruction errors, as well as errors introduced by event sampling. Simulation results are provided to illustrate the effectiveness of the proposed controllers.
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49
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Mofid O, Mobayen S. Adaptive sliding mode control for finite-time stability of quad-rotor UAVs with parametric uncertainties. ISA TRANSACTIONS 2018; 72:1-14. [PMID: 29224853 DOI: 10.1016/j.isatra.2017.11.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 11/04/2017] [Accepted: 11/24/2017] [Indexed: 06/07/2023]
Abstract
Adaptive control methods are developed for stability and tracking control of flight systems in the presence of parametric uncertainties. This paper offers a design technique of adaptive sliding mode control (ASMC) for finite-time stabilization of unmanned aerial vehicle (UAV) systems with parametric uncertainties. Applying the Lyapunov stability concept and finite-time convergence idea, the recommended control method guarantees that the states of the quad-rotor UAV are converged to the origin with a finite-time convergence rate. Furthermore, an adaptive-tuning scheme is advised to guesstimate the unknown parameters of the quad-rotor UAV at any moment. Finally, simulation results are presented to exhibit the helpfulness of the offered technique compared to the previous methods.
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Affiliation(s)
- Omid Mofid
- Electrical Engineering Department, University of Zanjan, Zanjan, Iran
| | - Saleh Mobayen
- Electrical Engineering Department, University of Zanjan, Zanjan, Iran.
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Xiang X, Liu C, Su H, Zhang Q. On decentralized adaptive full-order sliding mode control of multiple UAVs. ISA TRANSACTIONS 2017; 71:196-205. [PMID: 28941951 DOI: 10.1016/j.isatra.2017.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Revised: 07/24/2017] [Accepted: 09/11/2017] [Indexed: 06/07/2023]
Abstract
In this study, a novel decentralized adaptive full-order sliding mode control framework is proposed for the robust synchronized formation motion of multiple unmanned aerial vehicles (UAVs) subject to system uncertainty. First, a full-order sliding mode surface in a decentralized manner is designed to incorporate both the individual position tracking error and the synchronized formation error while the UAV group is engaged in building a certain desired geometric pattern in three dimensional space. Second, a decentralized virtual plant controller is constructed which allows the embedded low-pass filter to attain the chattering free property of the sliding mode controller. In addition, robust adaptive technique is integrated in the decentralized chattering free sliding control design in order to handle unknown bounded uncertainties, without requirements for assuming a priori knowledge of bounds on the system uncertainties as stated in conventional chattering free control methods. Subsequently, system robustness as well as stability of the decentralized full-order sliding mode control of multiple UAVs is synthesized. Numerical simulation results illustrate the effectiveness of the proposed control framework to achieve robust 3D formation flight of the multi-UAV system.
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Affiliation(s)
- Xianbo Xiang
- School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, China.
| | - Chao Liu
- Department of Robotics, LIRMM, UM-CNRS, UMR 5506, 161 Rue Ada, 34095 Montpellier, France.
| | - Housheng Su
- School of Automation, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, China.
| | - Qin Zhang
- State Key Lab of Digital Manufacturing, Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
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