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In this article, a model-free decentralized sliding mode control method is proposed based on adaptive dynamic programming algorithm to solve the problem of optimal trajectory tracking control of modular and reconfigurable robots. The dynamic model of modular and reconfigurable robot is formulated by a synthesis of joint subsystems with interconnected dynamic couplings. Based on sliding mode control technique, the optimal control problem of the modular and reconfigurable robot systems is transformed into an optimal compensation issue of unknown dynamics of each joint subsystems, in which the interconnected dynamic couplings effects among the subsystems are approximated by using the developed neural network identifier. Based on policy iteration scheme and the adaptive dynamic programming algorithm, the Hamilton-Jacobi-Bellman equation can be solved by using the critic neural network, so that optimal control policy can be obtained. The closed-loop system is proved to be asymptotically stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness of the method.

An event-triggered-based approximate optimal control is developed for modular robot manipulators (MRMs) using zero-sum game strategy. By utilizing the joint torque feedback method, robotic systems' dynamic model is formulated and state space equation is derived. According to event-triggered mechanism and neural network algorithm, the tracking control issue is converted to a zero-sum game optimal control issue. The optimal control policy and worst disturbance policy are acquired by Hamilton-Jacobi-Isaacs function respectively. The MRM system's tracking error is ultimately uniformly bounded. Finally, experiments demonstrate advantages of the method.

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