Optimal Track Control For Reconfigurable Manipulator With Actuator Saturation Based on Adaptive Dynamic Programming

This paper investigates a novel optimal tracking controller for reconfigurable manipulators with actuator saturation based on a technology called adaptive(approximate) dynamic programming (ADP). The Hamilton-Jacobi-Bellman (HJB) equation corresponding to constrained control is formulated using a suitable nonquadratic functional. Setup a critic neural network, network is utilized to achieve the policy iteration (PI) algorithm, The HJB equation could be solved cause the policy iteration (PI) algorithm, and then we can earn the approximated optimal saturation controller. Finally, the simulation study is given to show the effectiveness of the proposed optimal tracking controller.