Fault-tolerant tracker for a class of unknown interconnected large-scale sampled-data nonlinear systems with input constraint and actuator failure

Based on the model predictive control (MPC), the soft switching method, and the observer/Kalman filter identification (OKID) method, this paper presents the decentralized fault-tolerant trackers for a class of unknown interconnected large-scale multi-input multi-output sampled-data nonlinear systems with input constraint, actuator failure, and closed-loop decoupling properties. The off-line OKID method is utilized to determine decentralized appropriate (low-) order discrete-time linear models for the class of unknown interconnected large-scale sampled-data systems by using known input-output sampled data. Then, to overcome the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach will be presented. So, decentralized multiple MPC controllers are designed beforehand by using the identified linear models. Once a fault is detected in each decentralized controller, one of the backup control configurations in each decentralized subsystem is switched to using the soft switching approach. Thus, the decentralized fault-tolerant control with the closed-loop decoupling property can be achieved through the above approach with a high-gain property decentralized observer/tracker.