In this paper, a sliding mode (SM)-based online fault compensation control scheme is investigated for modular reconfigurable robots (MRRs) with actuator failures via adaptive dynamic programming. It consists of a SM-based iterative controller, an adaptive robust term and an online fault compensator. For fault-free MRR systems, the SM surface-based Hamilton–Jacobi–Bellman equation is solved by online policy iteration algorithm. The adaptive robust term is added to guarantee the reachable condition of SM surface. For faulty MRR systems, the actuator failure is compensated online to avoid the fault detection and isolation mechanism. The closed-loop MRR system is guaranteed to be asymptotically stable under the developed fault compensation control scheme. Simulation results verify the effectiveness of the present fault compensation control approach.

在本文中，通过自适应动态编程研究了具有执行器故障的模块化可重构机器人（MRR）的基于滑模（SM）的在线故障补偿控制方案。它由基于SM的迭代控制器，自适应鲁棒项和在线故障补偿器组成。对于无故障的MRR系统，通过在线策略迭代算法求解基于SM表面的Hamilton–Jacobi–Bellman方程。添加自适应鲁棒性术语以确保SM表面的可达到条件。对于有故障的MRR系统，执行器故障可以在线补偿，以避免故障检测和隔离机制。在已开发的故障补偿控制方案下，闭环MRR系统可以保证渐近稳定。仿真结果验证了当前故障补偿控制方法的有效性。