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.