Adaptive Dynamic Programming-Based Decentralized Guaranteed Cost Control for Reconfigurable Manipulators with Uncertain Environments

In this paper, a novel decentralized guaranteed cost control method is designed for reconfigurable manipulators with uncertain environments based on the adaptive dynamic programming (ADP) approach. Each joint module, which is the basic unit for constructing the reconfigurable manipulators, is regarded as a subsystem with model uncertainties that include the error of frictional modeling and the interconnection dynamic coupling (IDC) effect. Then, by employing a robust controller and a neural network (NN) identifier-based compensation controller, the decentralized guaranteed cost control issue with uncertain environments can be changed into the optimal control issue of reconfigurable manipulators. Based on ADP algorithm, the critic neural network is introduced to approximate the modified cost function, and then the Hamilton-Jacobi-Bellman equation is addressed by the policy iterative algorithm, thus making the obtention of approximate optimal control policy doable. The stability of the robotic system under the proposed control policy is demonstrated by employing the Lyapunov theory. Finally, the effectiveness of the proposed control policy for reconfigurable manipulators with different configurations is verified by simulation experiments.