Critic Learning-Based Control for Robotic Manipulators With Prescribed Constraints.

In this article, the optimal control problem for robotic manipulators (RMs) with prescribed constraints is addressed. Considering the environmental conditions and requirements of practical applications, prescribed constraints are imposed on the system states to guarantee the control performance and normal operation of the robotic system. Accordingly, an error transformation function is adopted to cope with the prescribed constraints and generate an equivalent unconstrained error for the convenience of the intelligent control design. In order to improve the learning ability and optimize the control performance, critic learning (CL) is introduced to the control design of the constrained RM based on the transformed equivalent unconstrained system. In addition, the stability analysis is given to illustrate the feasibility of the proposed CL-based control. Finally, simulations are conducted on a two-degree-of-freedom (DOF)-constrained RM to further validate the effectiveness of the proposed controller.

Actor-critic learning based coordinated control for a dual-arm robot with prescribed performance and unknown backlash-like hysteresis.

In this paper, we focus on the tracking problem of a dual-arm robot (DAR) with prescribed performance and unknown input backlash-like hysteresis. Considering this problem, adaptive coordinated control with actor-critic (AC) design is proposed. Motivated by the increasing control requirements, prescribed performance is imposed on the DAR system to guarantee the tracking performance. In order to improve the self-learning ability and handle the problems caused by the input backlash-like hysteresis and system uncertainty, AC learning (ACL) algorithm is introduced. Through the cost function about tracking errors, a critic network is adopted to judge the control performance. An actor network is adopted to obtain the control input based on the critic result, where the system uncertainty and unknown part of the input backlash-like hysteresis are approximated by neural networks (NNs). In addition, the system stability is proven by the Lyapunov direct method. Numerical simulation is finally conducted to further testify the validity of the proposed coordinated control with AC design for the DAR system.

Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties.

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.