Decentralized optimal control for modular and reconfigurable robots based on adaptive dynamic programming
Zixu Wang, Bo Lin Dong, Hongbing Xia, Yuanchun Li · 2017
This paper presents a decentralized optimal control method for modular and reconfigurable robots (MRRs) based on adaptive dynamic programming. First, the dynamic model of MRRs is formulated by using the Newton-Euler iterative algorithm, and then the state space description is given. Second, the optimal control policy of the MRRs system is obtained based on the policy iteration algorithm, which is used to solve the Hamilton-Jacobi-Bellman (HJB) equation via the critic neural network. Moreover, the stability of the closed-loop system is proved by using the Lyapunov theory. Finally, simulations are conducted to illustrate the effectiveness for the 2-DOF MRRs.