Abstract
The adaptive fixed-time control problem for nonlinear systems with time-varying actuator faults is investigated in this paper. A novel adaptive fixed-time controller is designed via combining the Lyapunov stability theory with the backstepping method. It can be adapted to both system uncertainties and unknown actuator faults. Compared with the existing fault-tolerant control schemes subject to actuator faults, the adaptive fixed-time neural networks control scheme can make sure that the tracking error is convergent in a small neighborhood of the origin within a fixed-time interval, and it does not depend on the original states of the system and actuator faults. In light of the control scheme proposed in this paper, the fixed-time stability of the closed-loop system can be guaranteed by theoretical analysis, and a numerical example is provided to verify the effectiveness of obtained theoretical results.
Highlights
In most practical control systems, sensor, actuator and the plant itself faults may occur at uncertain time, which can lead to poor performance and even instability of control systems
Lots of adaptive fault tolerant control (FTC) are introduced into existing research by using radial bais function neural networks (RBFNNs) [47]
A fixed-time control issue for nonlinear systems with unknown actuator failures has been addressed in this paper
Summary
In most practical control systems, sensor, actuator and the plant itself faults may occur at uncertain time, which can lead to poor performance and even instability of control systems. In [27] and [28], based on the backstepping technique, adaptive FTC approaches were designed to handle actuator faults in nonlinear systems where the FTC method runs through the entire backstepping control when there exist actuator faults and guarantee tracking precision. Due to existing uncertainties, a traditional form of fixed time stability convergence is hard to obtain To solve this dilemma, lots of adaptive FTC are introduced into existing research by using radial bais function neural networks (RBFNNs) [47]. Motivated by the above observations, a solution to cope with the fixed-time tracking issue for nonlinear systems subject to actuator faults is proposed in this work, in which the RBFNNs control strategy is to approximate unknown functions where both time-varying and bias fault are considered.
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