Decentralized Tracking Optimization Control for Partially Unknown Fuzzy Interconnected Systems via Reinforcement Learning Method

In this article, a novel parallel tracking control optimization algorithm is first proposed for partially unknown fuzzy interconnected systems. In the existing standard optimal tracking control, the bounded or nonasymptotic stable reference trajectory will lead the feedback control not converging to zero, which causes the performance index infinite and invalid. By using the precompensation technique, in this article, the working feedback control is considered as a reconstructed dynamic with the virtual control and a new augmented fuzzy interconnected tracking system is built, thus that the performance index is valid for optimal control. Then, combining the integral reinforcement learning (RL) method and decentralized control design, the novel integral RL parallel algorithm is first developed to solve the tracking controls for interconnected systems, which relax the requirements of exact matrices information and during the solving process. Both the convergence and stability of the designed control optimization scheme are guaranteed by theorems. Finally, the new parallel tracking algorithm for interconnected systems is verified through the dual-manipulator coordination system and simulation results demonstrate the effectiveness.