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 paper considers the problem of partial tracking errors constrained for high-order nonlinear multi-agent systems in strict-feedback form. In the control design, radial-based function neural networks are utilized to identify uncertain nonlinear functions, and a cooperative adaptive dynamic surface control is proposed to avoid the explosion of complexity in the backstepping technique. Based on the minimal learning parameter technique and the predefined performance approach, a novel cooperative adaptive neural network control method is developed. The proposed controller is able to guarantee that all the closed-loop network signals are cooperative semi-globally uniformly ultimately bounded, and partial tracking errors confine all times within the predefined bounds. Finally, simulation example and comparative example with previous methods are given to verify and clarify the effectiveness of the new design procedure.

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