Design and experimental verification: Health indicator-based decentralized optimal fault-tolerant control for modular robot manipulators via adjustable event-triggered mechanism

In this paper, a novel health indicator-based decentralized event-triggered optimal fault-tolerant control method is proposed for modular robot manipulators (MRMs). First, the subsystem dynamic model is established by utilizing the joint torque feedback (JTF) technique. Second, the developed global controller consists of a model based robust compensator and a health indicator-based optimal fault-tolerant controller, to address the model uncertainties and guarantee the faulty system stable operation, respectively. Considering the actuator output capability, a health indicator-based two-level judgment condition switching threshold strategy is designed for emergency treatment of sudden failures. On the basis of the neuro-dynamic programming (NDP) algorithm, the improved value function, reflecting the unknown faults and the health status of actuator, is approximated online under the event-triggered critic-only neural network structure. By addressing the Hamilton-Jacobi- Bellman equation, the health indicator-based decentralized event-triggered optimal fault-tolerant control strategy can be obtained. Finally, the tracking error of closed-loop MRM system can be demonstrated uniformly ultimately bounded through the Lyapunov theory. Furthermore, the experimental results obtained from the MRMs platform demonstrate the effectiveness of the proposed control method.