Disturbance Observer-Based Neural Network Control of Cooperative Multiple Manipulators With Input Saturation

A novel adaptive neural network-based time-delayed estimation control for nonlinear systems subject to disturbances and unknown dynamics

This paper presents an adaptive backstepping-based model-free control (BSMFC) for general high-order nonlinear systems (HNSs) subject to disturbances and unstructured uncertainties to enhance the system tracking performance. The proposed methodology is constructed based on the backstepping control (BSC) with radial basis function neural network (RBFNN) -based time-delayed estimation (TDE) to overcome the obstacle of unknown system dynamics. Additionally, a command-filtered (CF) approach is involved to address the complexity explosion of the BSC design. As the errors arising from approximation, new control laws are established to reduce the effects in this regard. The stability of the closed-loop system is guaranteed through the Lyapunov theorem and the superiority of the proposed methodology is confirmed through a comparative simulation with other model-free approaches.

Intelligent time-delay reduction of nonlinear model predictive control (NMPC) for wheeled mobile robots in the presence of obstacles

The speed of solving and processing factors that are beneficial in reaching the desired target is one of the problematic aspects of controlling robots that has been neglected by the majority of researchers. Therefore, it is essential to look into the factors that influence calculation speed and goal achievement, and there should be some solutions to control robots in a lower time without sacrificing accuracy. The speed of processing and operations in wheeled mobile robots (WMRs), as well as the speed of a nonlinear model predictive control (NMPC), are examined in this article. The "Prediction horizon", which is the most efficient element in increasing the calculations of the NMPC, is determined separately and intelligently at every step based on the magnitude of the error and the significance of the state variable by training a multilayer neural network, to decrease the time-delay in software mode. In addition, the processing speed in the hardware mode has increased due to the investigations conducted and the optimal selection of equipment effective in the speed of performing actuators, such as the use of the U2D2 interface instead of interface boards with their own processing, and the use of the pixy2 as a smart camera. The results have employed that the proposed intelligence method responds 40 to 50% faster compared to the conventional method of NMPC. Also, the path tracking error has been reduced by using the proposed algorithm due to the optimal gain extraction at each step. In addition, there is a comparison of solving speed in hardware mode between the proposed and usual methods. In this regard, about 33% increase in solving speed has been shown.

Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

Formation Tracking of Multiagent Systems With Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators

This article is concerned with a fault-tolerant formation tracking problem of nonlinear systems under unknown faults, where the leader's states are only accessible to a small set of followers via a directed graph. Under these faults, not only the amplitudes but also the signs of control coefficients become time-varying and unknown. The current setting will enhance the investigated problem's practical relevance and at the same time, it poses nontrivial design challenges of distributed control algorithms and convergence analysis. To solve this problem, a novel distributed control algorithm is developed by incorporating an estimation-based control framework together with a Nussbaum gain approach to guarantee an asymptotic cooperative formation tracking of nonlinear networked systems under unknown and dynamic actuator faults. Moreover, the proposed control framework is extended to ensure an asymptotic task-space coordination of multiple manipulators under unknown actuator faults, kinematics, and dynamics. Lastly, numerical simulation results are provided to validate the effectiveness of the proposed distributed designs.

A neural flexible PID controller for task-space control of robotic manipulators

This paper proposes an adaptive robust Jacobian-based controller for task-space position-tracking control of robotic manipulators. Structure of the controller is built up on a traditional Proportional-Integral-Derivative (PID) framework. An additional neural control signal is next synthesized under a non-linear learning law to compensate for internal and external disturbances in the robot dynamics. To provide the strong robustness of such the controller, a new gain learning feature is then integrated to automatically adjust the PID gains for various working conditions. Stability of the closed-loop system is guaranteed by Lyapunov constraints. Effectiveness of the proposed controller is carefully verified by intensive simulation results.

Enhanced compensation control of pneumatic muscle actuator with high-order modified dynamic model

Dynamic behaviour of the pneumatic muscle actuator (PMA) is conventionally modelled as a pressure-based first-order equation under discrete loads, which cannot exactly describe its dynamic features. Considering PMA's nonlinear, time-varying and hysteresis characteristics, we propose a novel high-order modified dynamic model of PMA based on its physical properties and working principle, with coefficients being identified under external dynamic loads. To tackle PMA's nonlinear hysteresis problem in high-frequency movements, a global fast terminal sliding mode controller with the modified model-based radial basis function (RBF) neural network disturbance compensator (RBF-GFTSMC) is designed. Comparison experimental studies are carried on a designed PMA platform that can provide continuously changing loads. Results show that the RBF-GFTSMC has superior trajectory tracking performance and disturbance compensation capability under wide-ranged frequencies and external loads, which can be potentially used to achieve precise control of PMA-actuated robots.

Robust Composite H∞ Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method

This article focuses on the composite H_∞ synchronization problem for jumping reaction-diffusion neural networks (NNs) with multiple kinds of disturbances. Due to the existence of disturbance effects, the performance of the aforementioned system would be degraded; therefore, improving the control performance of closed-loop NNs is the main goal of this article. Notably, for these disturbances, one of them can be described as a norm-bounded, and the other is generated by an exogenous model. In order to reject the above one kind of disturbance, a disturbance observer is developed. Furthermore, combining the disturbance observer approach and conventional state-feedback control scheme, a composite disturbance rejection controller is specifically designed to compensate for the influences of the disturbances. Then, some criteria are established based on the general Lyapunov stability theory, which can ensure that the synchronization error system is stochastically stable and satisfies a fixed H_∞ performance level. A simulation example is finally presented to verify the availability of our developed method.

Design and Analysis of Data-Driven Learning Control: An Optimization-Based Approach

Learning to perform perfect tracking tasks based on measurement data is desirable in the controller design of systems operating repetitively. This motivates this article to seek an optimization-based design and analysis approach for data-driven learning control systems by focusing on iterative learning control (ILC) of repetitive systems with unknown nonlinear time-varying dynamics. It is shown that perfect output tracking can be realized with updating inputs, where no explicit model knowledge but only measured input-output data are leveraged. In particular, adaptive updating strategies are proposed to obtain parameter estimations of nonlinearities. A double-dynamics analysis approach is applied to establish ILC convergence, together with boundedness of input, output, and estimated parameters, which benefits from employing properties of nonnegative matrices. Simulations are implemented to verify the validity of our optimization-based adaptive ILC.

Cooperative and robust object handling by multiple manipulators based on the differential equation approximator

The problem of transporting a rigid object by a team of robotic arms is studied in this article. The study aims to enable manipulators to move the object in the desired and predefined direction efficiently. It is also desired to keep the force applied to the object limited to avoid distortion of its shape. Since practical systems usually confront limitations such as lack of complete system model information and disturbances, the presented approach should confront the effects of such phenomena on the performance. To fulfill the purpose, based on the universal approximation property, a powerful approximator for estimating uncertainties and disturbances based on differential equations is presented. Then a robust approximator-controller structure is designed to solve the multi-objective issue of position, orientation, and force control in the cooperating robotic system. To confirm a stable object grasp, and transportation, Lyapunov analysis is utilized and based on a thorough mathematical analysis, it is shown that the presented approximator-controller structure assures system stability. Finally, the proposed scheme is applied to two three-degree-of-freedom (3-DOF) robot arms that cooperatively handle a common and rigid object. Various tests are performed assuming the unavailability of system model information and in the existence of perturbations, and the results are analyzed.

Finite-Time Command-Filtered Composite Adaptive Neural Control of Uncertain Nonlinear Systems

This article presents a new command-filtered composite adaptive neural control scheme for uncertain nonlinear systems. Compared with existing works, this approach focuses on achieving finite-time convergent composite adaptive control for the higher-order nonlinear system with unknown nonlinearities, parameter uncertainties, and external disturbances. First, radial basis function neural networks (NNs) are utilized to approximate the unknown functions of the considered uncertain nonlinear system. By constructing the prediction errors from the serial-parallel nonsmooth estimation models, the prediction errors and the tracking errors are fused to update the weights of the NNs. Afterward, the composite adaptive neural backstepping control scheme is proposed via nonsmooth command filter and adaptive disturbance estimation techniques. The proposed control scheme ensures that high-precision tracking performances and NN approximation performances can be achieved simultaneously. Meanwhile, it can avoid the singularity problem in the finite-time backstepping framework. Moreover, it is proved that all signals in the closed-loop control system can be convergent in finite time. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.

Output-Based Event-Triggered Cooperative Robust Regulation for Constrained Heterogeneous Multiagent Systems

The output-based event-triggered cooperative output regulation problem is addressed for constrained linear heterogeneous multiagent system in this article. In light of the robust control theory, H_∞ leader-following consensus with respect to exogenous signals, including both disturbance to be rejected and reference state of leader to be tracked, is guaranteed. Meanwhile, the system performance alleviates degradation through a model recovery anti-windup technique while encountering input saturation. Furthermore, the follower's self-state observer, the leader-state observer, and the anti-windup auxiliary system are integrated into a comprehensive system, and a unified event-triggering mechanism of full states is addressed. A fixed lower bound of sampled interval is adopted such that the frequency of data transmission gets reduced and no Zeno-behavior happens. Both the input and output of the follower's controller and anti-windup compensator hold constant, respectively, during the event-triggered intervals such that the resulting output-based event-triggered controller can be directly implemented in a digital platform. Finally, a simulation example is provided to illustrate the effectiveness.

Multi-agent collaborative control parameter prediction for intelligent precision loading

Due to the low adjustment accuracy of manual prediction, conventional programmable logic controller systems can easily lead to inaccurate and unpredictable load problems. The existing multi-agent systems based on various deep learning models has weak ability for advanced multi-parameter prediction while mainly focusing on the underlying communication consensus. To solve this problem, we propose a hybrid model based on a temporal convolutional network with the feature crossover method and light gradient boosting decision trees (called TCN-LightGBDT). First, we select the initial dataset according to the loading parameters' tolerance range and supply supplementing method for the deviated data. Second, we use the temporal convolutional network to extract the hidden data features in virtual loading areas. Further, a two-dimensional feature matrix is reconstructed through the feature crossover method. Third, we combine these features with basic historical features as the input of the light gradient boosting decision trees to predict the adjustment values of different combinations. Finaly, we compare the proposed model with other related deep learning models, and the experimental results show that our model can accurately predict parameter values.

Pneumatic Soft Robots: Challenges and Benefits

In the field of robotics, soft robots have been showing great potential in the areas of medical care, education, service, rescue, exploration, detection, and wearable devices due to their inherently high flexibility, good compliance, excellent adaptability, and natural and safe interactivity. Pneumatic soft robots occupy an essential position among soft robots because of their features such as lightweight, high efficiency, non-pollution, and environmental adaptability. Thanks to its mentioned benefits, increasing research interests have been attracted to the development of novel types of pneumatic soft robots in the last decades. This article aims to investigate the solutions to develop and research the pneumatic soft robot. This paper reviews the status and the main progress of the recent research on pneumatic soft robots. Furthermore, a discussion about the challenges and benefits of the recent advancement of the pneumatic soft robot is provided.

Fractional-Order Adaptive Fault-Tolerant Synchronization Tracking Control of Networked Fixed-Wing UAVs Against Actuator-Sensor Faults via Intelligent Learning Mechanism

This article presents an enhanced fault-tolerant synchronization tracking control scheme using fractional-order (FO) calculus and intelligent learning architecture for networked fixed-wing unmanned aerial vehicles (UAVs) against actuator and sensor faults. To increase the flight safety of networked UAVs, a recurrent wavelet fuzzy neural network (RWFNN) learning system with feedback loops is first designed to compensate for the unknown terms induced by the inherent nonlinearities, unexpected actuator, and sensor faults. Then, FO sliding-mode control (FOSMC), involving the adjustable FO operators and the robustness of SMC, are dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of the RWFNN learning system embedded in the networked fixed-wing UAVs are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing UAVs can synchronously track their references with bounded tracking errors. Finally, comparative simulations and hardware-in-the-loop experiments are conducted to demonstrate the validity of the proposed control scheme.

Adaptive Neural-Network Boundary Control for a Flexible Manipulator With Input Constraints and Model Uncertainties

This article develops an adaptive neural-network (NN) boundary control scheme for a flexible manipulator subject to input constraints, model uncertainties, and external disturbances. First, a radial basis function NN method is utilized to tackle the unknown input saturations, dead zones, and model uncertainties. Then, based on the backstepping approach, two adaptive NN boundary controllers with update laws are employed to stabilize the like-position loop subsystem and like-posture loop subsystem, respectively. With the introduced control laws, the uniform ultimate boundedness of the deflection and angle tracking errors for the flexible manipulator are guaranteed. Finally, the control performance of the developed control technique is examined by a numerical example.

Finite-Time Dynamic Tracking Control of Parallel Robots with Uncertainties and Input Saturation

This paper considers the finite-time dynamic tracking control for parallel robots with uncertainties and input saturation via a finite-time nonsingular terminal sliding mode control scheme. A disturbance observer is designed to estimate the lumped disturbance in the dynamic model of the parallel robot, including modeling errors, friction and external disturbance. By introducing the fractional exponential powers into the existing asymptotic convergent auxiliary system, a novel finite-time convergent auxiliary system is constructed to compensate for input saturation. The finite-time nonsingular terminal sliding mode control is proposed based on the disturbance estimation and the state of the novel auxiliary system, so that the convergence performance, control accuracy and robustness are improved. Due to the estimation and compensation for the lumped disturbance, the inherent chattering characteristic of sliding mode control can be alleviated by reducing the control gain. The finite-time stability of the closed-loop system is proved with Lyapunov theory. Finally, simulation and experimental research on the dynamic control of a conveying parallel robot are carried out to verify the effectiveness of the proposed method.