Neuroadaptive backstepping tracking control of robotic manipulators considering actuator dynamics

Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model

With the development of robot control theory and the requirement for robot to provide better performance in uncertain working environment, more and more research interests have been attracted to the two research topics of dealing with kinematic uncertainty existing in the robot system and considering actuator dynamics into the controller design during the past one decade. However, the studies in these two research areas have been developing separately in the literature. This thesis is dedicated to solve the adaptive control problems of robot in the presence of all uncertainties in the kinematics, manipulator dynamics and actuator dynamics. The main contribution of the thesis is that a novel adaptive regressor design method is proposed which enables the adaptation of different kinds of uncertainties simultaneously during the control process. Based on the novel regressor design, adaptive control methods have been proposed for regulation and tracking control of robot with uncertain kinematics, manipulator and actuator dynamics. These approaches constitute the first results in the research area of robot control to deal with both kinematic and actuator dynamic uncertainties. Through Lyapunov analysis, the stability of the closed-loop systems are shown to be guaranteed and the sufficient conditions to be satisfied are explicitly presented.

In this paper, we mainly solve the adaptive control problem of robot manipulators with uncertain kinematics, dynamics, and actuators parameters, which has been a long-standing, yet unsolved problem in the robotics field, because of the technical difficulties in handling highly coupled effect between control torque and the mentioned uncertainties. To overcome the difficulties, we propose a new Lyapunov-based adaptive control methodology, which effectively fuses the inverse Jacobian technique and the actuator adaptation law, with which the chattering in tracking errors caused by actuator parameter perturbation is well suppressed. It is demonstrated that the asymptotic convergence of all closed-loop signals is guaranteed. Moreover, the effectiveness of our control scheme is illustrated through simulation studies.

This work proposes a non-singular fast terminal sliding mode control (NFTSMC) strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and a nonlinear disturbance observer (NDO) to address the issues of modeling errors, motion disturbances, and transmission friction in robotic manipulators. Firstly, a novel modular serial 5-DOF robotic manipulator configuration is designed, and its kinematic and dynamic models are established. Secondly, a nonlinear disturbance observer is employed to estimate the total disturbance of the system and apply feedforward compensation. Based on boundary layer technology, an improved NFTSMC method is proposed to accelerate the convergence of tracking errors, reduce chattering, and avoid singularity issues inherent in traditional terminal sliding mode control. The stability of the designed control system is proved using Lyapunov stability theory. Subsequently, a deep reinforcement learning (DRL) agent based on the TD3 algorithm is trained to adaptively adjust the control gains of the non-singular fast terminal sliding mode controller. The dynamic information of the robotic manipulator is used as the input to the TD3 agent, which searches for optimal controller parameters within a continuous action space. A composite reward function is designed to ensure the stable and efficient learning of the TD3 agent. Finally, the motion characteristics of three joints for the designed 5-DOF robotic manipulator are analyzed. The results show that compared to the non-singular fast terminal sliding mode control algorithm based on a nonlinear disturbance observer (NDONFT), the non-singular fast terminal sliding mode control algorithm integrating a nonlinear disturbance observer and the Twin Delayed Deep Deterministic Policy Gradient algorithm (TD3NDONFT) reduces the mean absolute error of position tracking for the three joints by 7.14%, 19.94%, and 6.14%, respectively, and reduces the mean absolute error of velocity tracking by 1.78%, 9.10%, and 2.11%, respectively. These results verify the effectiveness of the proposed algorithm in enhancing the trajectory tracking accuracy of the robotic manipulator under unknown time-varying disturbances and demonstrate its strong robustness against sudden disturbances.

In this paper, backstepping strategy is used to design an adaptive tracking controller for rigid-link electrically driven robots in the presence of uncertainties in kinematics, manipulator dynamics and actuator dynamics. First, the armature current vector is regarded as the control variable for the manipulator subsystem and a desired armature current is designed based on the inverse manipulator dynamics. Then, an input control voltage is designed so that armature current tracks the desired armature current. Simulation results of applying the proposed controller on a two link RLED robot are presented to show the effectiveness of the proposed control scheme.

The trajectory tracking problem of a class of robot manipulators is investigated by a simpler design adaptive neural network(NN) in this paper. The Radial Basis Function(RBF) NN is utilized to handle the uncertainties of the dynamics. Compared with the traditional schemes, the dimension of the input vectors of RBFNN is decrease from $4n$ to $3n$ but it have equal tracking and approximation performances. The output feedback control is considered when the velocity information cannot be obtained. Moreover, the weights of RBFNN converge to its optimal value by using the auxiliary filter to estimate weights error. The robot manipulator system is semi-globally and uniformly bounded which is proved by Lyapunov's theory. Simulation results demonstrate that the simpler controller has the same capability compared with the non-simplified method.

Robust adaptive control of a class of nonlinear mechanical systems with unbounded and fast-varying uncertainties

A novel hierarchical control framework combining computed-torque-like control (CTLC) with disturbance-observer-based event-triggered robust model predictive control (DO-ET-RMPC) is proposed for the trajectory tracking control of robotic manipulators with bounded disturbances and state and control input constraints. The CTLC approach is first used to cancel the exact nonlinear dynamics of the original tracking error system to obtain a set of decoupling linear tracking error subsystems, thus reducing the optimization complexity of model predictive control (MPC). The composite DO-ET-RMPC scheme is then developed based on the so-called dual-mode MPC approach to robustly stabilize the tracking error subsystems, which could improve the robustness of MPC and save its computational resources simultaneously. The continuous-time theoretical properties of the DO-ET-RMPC scheme, considering disturbances and state and control input constraints simultaneously, are provided for the first time, including the avoidance of Zeno behavior, robust constraint satisfaction, recursive feasibility, and stability. In the end, the superiorities of the proposed control scheme are verified by the comparative simulations.

In this paper, a robust output feedback tracking control scheme for uncertain robot manipulators with only position measurements is investigated. First, a quasi-continuous second-order sliding mode (QC2S)-based exact differentiator and super-twisting second-order sliding mode (STW2S) controllers are designed to guarantee finite time convergence. Although the QC2S produces continuous control and less chattering than that of a conventional sliding mode controller and other high-order sliding mode controllers, a large amount of chattering exists when the sliding manifold is defined by the equation [Formula: see text]. To decrease the chattering, an uncertainty observer is used to compensate for the uncertainty effects, and this controller may possess a smaller switching gain. Compared to the QC2S controller, the STW2S has less chattering and tracking error when the system remains on the sliding manifold [Formula: see text]. Therefore, to further eliminate the chattering and obtain a faster transient response and higher tracking precision, we develop a quasi-continuous super-twisting second-order sliding mode controller, which integrates both the merits of QC2S and STW2S controllers. The stability and convergence of the proposed scheme are theoretically demonstrated. Finally, computer simulation results for a PUMA560 robot comparing with conventional QC2S and STW2S controllers are shown to verify the effectiveness of the proposed algorithm.

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.

In this paper, a controller for robot manipulators is proposed to guarantee the tracking error of the systems bounded by predefined decreasing boundary. In this control scheme, a multi-layer neural network is used to approximate the unknown non-linear items, and the robustifying control term is used to compensate the approximation errors. The adaptive laws of weights of the neural network and robustifying control term are derived based on the Lyapunov stability analysis, so that, under appropriate assumptions, the transient and steady-state error bounds can be guaranteed. Compared with the existing work, the adaptable parameters in the proposed method do not need an off-line training procedure for better approximation. Simulations performed on a two-link robot manipulator illustrate the developed controller and demonstrate its performance.

This paper proposes two types of adaptive control schemes combined by the conventional computed-torque control and different fuzzy compensators for the robust tracking control of robotic manipulators with structured and unstructured uncertainties. Fuzzy compensators based on feed-forward and feed-back are developed to compensate these modeling uncertainties. The validity of the two types of adaptive control schemes is shown by numerical simulations of a three link rotary robot manipulator.

The tracking control of a robot manipulator in the presence of uncertainties is considered, where the robot manipulator and its actuating system are treated as a set of interconnected sub-systems with bounded uncertainties. Utilizing the local states as well as the states of the connecting sub-systems, a class of decentralized continuous non-linear feedback control law is presented for the tracking problem of the robotic system. The design of the controller is based on the deterministic approach.

SUMMARYIn this paper, the backstepping strategy is used to design an adaptive tracking controller for rigid-link electrically driven robots in the presence of parametric uncertainties in kinematics, manipulator dynamics, and actuator dynamics. To avoid acceleration measurements, two techniques are exploited. One technique adds compensation control terms to the control law signal. The other uses a linear in variable property of the Jacobian matrix. Global asymptotic convergence of the end-effector motion tracking errors is shown via Lyapunov analysis. Simulation results are presented to show the effectiveness of the proposed control scheme.

To deal with the problem of the output feedback tracking (OFT) control with bounded torque inputs of robot manipulators, we propose a generalized fuzzy saturated OFT controller based on singular perturbation theory. First, considering the fact that the output toque of joint actuators is limited, a general expression for a class of saturation functions is given to be applied in the control law. Second, to carry out the whole closed-loop control with only position measurements, linear and nonlinear filters are optionally involved to generate a pseudo signal to surrogate the actual velocity tracking error. As a third contribution, a fuzzy regulator is added to obtain a self-tuning performance in tackling the disturbances. Moreover, an explicit but strict stability proof of the system based on the stability theory of singularly perturbed systems is presented. Finally, numerical simulations on several sample controllers are implemented to verify the effectiveness of the proposed approach.