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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.
Hashemi et al. (Mon,) studied this question.