Adaptive decentralised dynamic surface control for non-linear large-scale systems against actuator failures

This study presents an adaptive decentralised dynamic surface control (DSC) approach for a class of large-scale non-linear systems with unknown non-linear functions, unknown control gains, time-varying delays and in the presence of unknown actuator failures. The considered actuator failures are modelled to cover both loss of effectiveness and stuck at some time-varying values where the values, patterns and time occurrences of the failures are unknown. With the help of neural networks to approximate the unknown non-linear functions, a novel adaptive actuator failure compensation controller is developed based on the backstepping method and the DSC approach. The appropriate Lyapunov-Krasovskii functionals are introduced to design new adaptive laws to compensate the unknown actuator failures as well as uncertainties from unknown non-linearities and time delays. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed-loop signals is guaranteed and the tracking errors are proved to converge to a small neighbourhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark and a chemical reactor system. The simulation results show the effectiveness of the proposed method.