In this paper, an adaptive actuator failure compensation scheme is proposed for a class of parametric-strict-feedback multi-input multi-output nonlinear systems with unknown time-varying state delays. The considered actuator failures are types of loss of effectiveness, in which unknown system inputs may lose unknown fraction of their effectiveness. The adaptive compensation controller is constructed by utilizing a backstepping design method. The appropriate Lyapunov-Krasovskii functionals are introduced to design new adaptive laws to compensate the unknown actuator failures as well as uncertainties from unknown parameters and state delays. The boundedness of all the closed-loop signals is guaranteed, and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach.

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This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov-Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.

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