Value Iteration-based Decentralized Fuzzy Optimal Control of Modular Reconfigurable Robots via Adaptive Dynamic Programming

In this article, a decentralized fuzzy optimal control method dependent on value iteration (VI) is proposed to settle the optimal trajectory tracking control problem of modular reconfigurable robots (MRRs) under the adaptive dynamic programming (ADP) algorithm. A fuzzy logic system (FLS) is designed to estimate cost function and unknown system dynamics of MRRs respectively. Through the use of ADP method, construct a local online VI algorithm and solving HJB equation with approximate cost function, then the decentralized FLS-based optimal control method is realized. On the basis of Lyapunov theorem the MRR system is proved to be asymptotically stable. At last, simulation results show the availability of the suggested method.