Disturbance Observer-Based Dynamic Surface Control of Transport Aircraft With Continuous Heavy Cargo Airdrop

Adaptive Control of Uncertain Nonlinear Systems via Event-Triggered Communication and NN Learning

This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.

Quadrotor Real-Time Simulation: A Temporary Computational Complexity-Based Approach

The interaction of digital systems with dynamic systems requires synchrony and the accomplishment of time constrains, so the simulation of physical processes needs an implementation by means of real-time systems (RTS). However, as it can be expected, every simulation and/or implementation might demand too many computational resources, surpassing the capacity of the processor used by computational systems. This is the reason for the need to perform a temporary computational complexity analysis based on the study of the behavior of the execution times of the implemented simulation. In this regard, the Real-Time Operating Systems (RTOS) feature time managing tools, which allow their precise measurement and the establishment of scheduling criteria in process execution. Therefore, this research proposes accomplishing a temporary computational complexity analysis of the real-time simulation by an embedded system (ES) of an unmanned aerial vehicle (UAV) propelled by four rotors. Derived from this analysis, formal definitions are elaborated and proposed, which establish a close relationship between the temporary computational complexity and typical real-time temporary constraints. To the best of the author’s knowledge, the definitions presented in this article have not been reported in the literature. Furthermore, to perform the temporary computational complexity analysis of the UAV, the mathematical modeling based on the Euler–Lagrange approach is presented in detail. Finally, simulations were performed using a real-time system implemented on the Embedded Computer System (ECS) Raspberry Pi 2 Model B+, in order to validate the suggested definitions.

TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically

Tracking aircraft and parachutes plays a vital role in airdrop experiments. It is necessary to study a parachute’s open state and flight trajectory. More scholars are looking into how to efficiently and accurately obtain parachute deformation data and trajectory data. At present, the actual data collection primarily involves experimenters holding high-definition high-speed cameras to track and shoot parachutes to obtain the image sequences of the parachutes during the airdrop process. However, these methods cannot obtain the trajectories of the parachutes and they are susceptible to interference from human factors. In this paper, we designed TuSeSy, an intelligent turntable servo system that can track the aircraft and parachutes in airdrop tests automatically. Specifically, TuSeSy generates the control commands according to the differences between the actual taken images and the inferred images by tracking algorithms (so as to actually track the target). In addition, we propose an effective multi-target tracking switch algorithm based on the image frame difference and optical flow, to achieve real-time switching from the aircraft to the parachute in an airdrop test. To evaluate the performance of TuSeSy, we conducted extensive experiments; the experimental results show that TuSeSy not only solves the problem of wrong target tracking, but it also reduces computational overhead. Moreover, the multi-target tracking switch algorithm has higher computing efficiency and reliability compared to other tracking switch approaches, ensuring the practical applications of the turntable servo system.

Distributed Adaptive Fault-Tolerant Time-Varying Formation Control of Unmanned Airships With Limited Communication Ranges Against Input Saturation for Smart City Observation

This article investigates the distributed fault-tolerant time-varying formation control problem for multiple unmanned airships (UAs) against limited communication ranges and input saturation to achieve the safe observation of a smart city. To address the strongly nonlinear functions caused by the time-varying formation flight with limited communication ranges and bias faults, intelligent adaptive learning mechanisms are proposed by incorporating fuzzy neural networks. Moreover, Nussbaum functions are introduced to handle the input saturation and loss-of-effectiveness faults. The distinct features of the proposed control scheme are that time-varying formation flight, actuator faults including bias and loss-of-effectiveness faults, limited communication ranges, and input saturation are simultaneously considered. It is proven by Lyapunov stability analysis that all UAs can achieve a safe formation flight for the smart city observation even in the presence of actuator faults. Hardware-in-the-loop experiments with open-source Pixhawk autopilots are conducted to show the effectiveness of the proposed control scheme.

Composite prescribed performance control of small unmanned aerial vehicles using modified nonlinear disturbance observer

An integrated control scheme composed of modified nonlinear disturbance observer and predefined-time prescribed performance control is proposed to address the high-accuracy tracking problem of the unmanned aerial vehicles (UAVs) subjected to external mismatched disturbances. By utilizing the transformation technique that incorporates the desired performance characteristic and the newly predefined-time performance function, the original controlled system can be transformed into a new unconstrained one to achieve the fixed-time convergence of the tracking error. Then, by virtual of the transformed unconstrained system, a modified nonlinear disturbance observer (NDO) which possesses fast convergence speed is established to estimate the external disturbance. With the application of the precise estimation value to compensate the normal control design in each back-stepping step, a novel composite control scheme is constructed. The light spot of the proposed scheme is that it not only has the superior capability to attenuate unknown mismatched disturbances, but also can guarantee that the output tracking errors converge to their prescribed regions within predefined time. Finally, simulation studies verify the effectiveness of the proposed control scheme.

A novel adaptive control design method for stochastic nonlinear systems using neural network

This paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called “explosion of complexity”, is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl–Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.

Neural-Network-Based Adaptive Funnel Control for Servo Mechanisms With Unknown Dead-Zone

This paper proposes an adaptive funnel control (FC) scheme for servo mechanisms with an unknown dead-zone. To improve the transient and steady-state performance, a modified funnel variable, which relaxes the limitation of the original FC (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor. Then, by applying the error transformation method, the original error is transformed into a new error variable which is used in the controller design. By using an improved funnel function in a dynamic surface control procedure, an adaptive funnel controller is proposed to guarantee that the output error remains within a predefined funnel boundary. A novel command filter technique is introduced by using the Levant differentiator to eliminate the "explosion of complexity" problem in the conventional backstepping procedure. Neural networks are used to approximate the unknown dead-zone and unknown nonlinear functions. Comparative experiments on a turntable servo mechanism confirm the effectiveness of the devised control method.

Adaptive Sliding Mode Trajectory Tracking Control for WMR Considering Skidding and Slipping via Extended State Observer

When the wheeled mobile robot (WMR) is required to perform specific tasks in complex environment, i.e., on the forestry, wet, icy ground or on the sharp corner, wheel skidding and slipping inevitably occur during trajectory tracking. To improve the trajectory tracking performance of WMR under unknown skidding and slipping condition, an adaptive sliding mode controller (ASMC) design approach based on the extended state observer (ESO) is presented. The skidding and slipping is regarded as external disturbance. In this paper, the ESO is introduced to estimate the lumped disturbance containing the unknown skidding and slipping, parameter variation, parameter uncertainties, etc. By designing a sliding surface based on the disturbance estimation, an adaptive sliding mode tracking control strategy is developed to attenuate the lumped disturbance. Simulation results show that higher precision tracking and better disturbance rejection of ESO-ASMC is realized for linear and circular trajectory than the ASMC scheme. Besides, experimental results indicate the ESO-ASMC scheme is feasible and effective. Therefore, ESO-ASMC scheme can enhance the energy efficiency for the differentially driven WMR under unknown skidding and slipping condition.

Rapid trajectory planning of a reusable launch vehicle for airdrop with geographic constraints

Online feasible trajectory generation for an airdrop unpowered reusable launch vehicle is addressed in this article. A rapid trajectory planning algorithm is proposed to satisfy not only the multiple path and terminal constraints but also the complex geographic constraints of waypoints and no-fly zones. Firstly, the lower and upper boundaries of the bank angle that implement all the path constraints are obtained based on the quasi-equilibrium glide condition. To determine the bank angle directly, a weighted interpolation of the boundaries is then developed, which provides an effective approach to simplify the planning process as a one-parameter search problem. Subsequently, three types of lateral planning algorithms are designed to determine the sign of the bank angle according to the requirements of waypoints passage, no-fly-zones avoidance, and terminal constraints in the airdrop process, and the convergence of these methods for passing over the waypoints and meeting the terminal conditions has been clarified and formally demonstrated. Considering the constraints in the actual airdrop flight missions, the planning trajectory is divided into several subphases to facilitate the application of corresponding algorithms. Finally, the performance of the proposed algorithm is assessed through three airdrop missions of reusable launch vehicle with different geographic constraints. Besides, the effectiveness of the algorithm is demonstrated by the Monte Carlo simulation results.

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

Composite Intelligent Learning Control of Strict-Feedback Systems With Disturbance

This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set. The highlight is that different from previous work directly toward asymptotic stability, the transparency of the intelligent approximation and disturbance estimation is included in the control scheme. The uniformly ultimate boundedness stability is analyzed via Lyapunov method. Through simulation verification, the composite intelligent learning with disturbance observer can efficiently estimate the effect caused by system nonlinearity and disturbance while the proposed approach obtains better performance with higher accuracy.

Influence of the ground effect on airdrop mission performance analysis

Aimed at determining the ground effect’s influence on the process of ultralow-altitude airdrop, this article studies the corresponding changes in aerodynamic characteristics caused by the ground effect. Through analyzing the longitudinal long- and short-period modes and the lateral mode, this work evaluates the impact that the ground effect has on the level-off and the traction stages during an airdrop mission, with reference to corresponding flight quality standards. Accordingly, these findings provide a reference for the design of flight control regarding airdrop capabilities and support a theory for corresponding ground experiments.

Cargo blocking failure analysis, simulation, and safety control of transport aircraft with continuous heavy airdrop

This article investigates the cargo’s blocking failure analysis, simulation, and safety control of transport aircraft with continuous heavy airdrop. As the cargos move backward and drop out, the continuous variation of center of gravity for the whole system will deteriorate flight quality dramatically. Furthermore, due to various mechanical reasons, if the cargo is blocked on the delivery channel and the airdrop process is suspended suddenly at this time, the flight safety may be threatened. In view of this, the blocking failure is analyzed based on the aircraft model in this article and then the simulation is completed to show the failure’s impact on aircraft’s flight quality. Next, based on the uncertainty analysis and introduction of interval robust control theory, the safety controller is designed to stable the closed-loop system. The final simulation shows the proposed safety control strategy’s effectiveness.

Backstepping control based onL1adaptive theory for large transport aircraft heavy load airdrop

A study of the L1adaptive controller is conducted based on the backstepping method for the model of large transport aircraft with drastic changes appearing in heavy load airdrop process. The system is divided into an attitude subsystem and a velocity subsystem. For the attitude subsystem, the backstepping control is used to design the virtual control of path angle and the pitch angle in external loop with the L1adaptive controller designed in internal loop to estimate the uncertainties and disturbances in the subsystem and to compensate them. In the stability analysis, the uniform boundedness of all signals in the closed-loop system is proven. Simulation results show that the proposed control method preserves the quick dynamic torque response, high efficiency, and robustness in heavy load airdrop; to some extent it can alleviate the control switch lead or lag problem and ensure the safety of the transport aircraft to fulfill the complete airdrop mission.

Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints

The control problem of an uncertain n -degrees of freedom robotic manipulator subjected to time-varying output constraints is investigated in this paper. We describe the rigid robotic manipulator system as a multi-input and multi-output nonlinear system. We devise a disturbance observer to estimate the unknown disturbance from humans and environment. To solve the uncertain problem, a neural network which utilizes a radial basis function is used to estimate the unknown dynamics of the robotic manipulator. An asymmetric barrier Lyapunov function is employed in the process of control design to avert the contravention of the time-varying output constraints. Simulation results validate the validity of the presented control scheme.

Memoryless disturbance-observer-based adaptive tracking of uncertain pure-feedback nonlinear time-delay systems with unmatched disturbances

This paper presents a delay-independent nonlinear disturbance observer (NDO) design methodology for adaptive tracking of uncertain pure-feedback nonlinear systems in the presence of unknown time delays and unmatched external disturbances. Compared with all existing NDO-based control results for uncertain lower-triangular nonlinear systems where unknown time delays have been not considered, the main contribution of this paper is to develop a delay-independent design strategy to construct an NDO-based adaptive tracking scheme in the presence of unknown time-delayed nonlinearities and non-affine nonlinearities unmatched in the control input. The proposed delay-independent scheme is constructed by employing the appropriate Lyapunov-Krasovskii functionals and the same function approximators for the NDO and the controller. It is shown that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin.

Consensus Control of Nonlinear Multiagent Systems With Time-Varying State Constraints

In this paper, we present a novel adaptive consensus algorithm for a class of nonlinear multiagent systems with time-varying asymmetric state constraints. As such, our contribution is a step forward beyond the usual consensus stabilization result to show that the states of the agents remain within a user defined, time-varying bound. To prove our new results, the original multiagent system is transformed into a new one. Stabilization and consensus of transformed states are sufficient to ensure the consensus of the original networked agents without violating of the predefined asymmetric time-varying state constraints. A single neural network (NN), whose weights are tuned online, is used in our design to approximate the unknown functions in the agent's dynamics. To account for the NN approximation residual, reconstruction error, and external disturbances, a robust term is introduced into the approximating system equation. Additionally in our design, each agent only exchanges the information with its neighbor agents, and thus the proposed consensus algorithm is decentralized. The theoretical results are proved via Lyapunov synthesis. Finally, simulations are performed on a nonlinear multiagent system to illustrate the performance of our consensus design scheme.