In this paper, a novel decentralized guaranteed cost control method is designed for reconfi gurable manipulators with uncertain environments based on the adaptive dynamic programming (ADP) approach. Each joint module, which is the basic unit for constructing the reconfi gurable manipulators, is regarded as a subsystem with model uncertainties that include the error of frictional modeling and the interconnection dynamic coupling (IDC) eff ect. Then, by employing a robust controller and a neural network (NN) identifi er-based compensation controller, the decentralized guaranteed cost control issue with uncertain environments can be changed into the optimal control issue of reconfi gurable manipulators. Based on ADP algorithm, the critic neural network is introduced to approximate the modifi ed 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 eff ectiveness of the proposed control policy for reconfi gurable manipulators with diff erent confi gurations is verifi ed by simulation experiments.
KCI
