Abstract
This research focuses on addressing challenges like input-deadzone, external disturbances, and sensor and actuator faults in switched nonlinear systems with strict-feedback form through the introduction of an adaptive finite-time fault tolerant control (FTC) strategy. To mitigate errors and approximate unknown functions, radial basis function neural networks are leveraged as part of the solution. By combining neural networks with the backstepping technique, an adaptive finite-time tracking controller is formulated. The utilisation of a common Lyapunov function (CLF) for stability analysis ensures that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Moreover, the tracking error is guaranteed to converge within a finite time to a defined compact set. The effectiveness of the proposed control methodology is validated through the successful demonstration in two simulation scenarios, including a practical electromechanical system.
Published Version
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