Abstract
This paper investigates a decentralized guaranteed cost control method of modular and reconfigurable robots (MRRs) based on adaptive dynamic programming (ADP) algorithm. First, we formulate the dynamic model of systems and the interconnected couplings are described. Second, we design a decentralized guaranteed cost controller. By establishing a critic neural network, a learning-based adaptive dynamic programming algorithm is proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation, and then the approximate optimal control policy can be derived. The asymptotic stability of the closed-loop system is proved based on the Lyapunov theory. Finally, simulations are performed for the 2-DOF MRRs to verify the effectiveness of the proposed method.
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