In this article, a novel data-driven sliding mode controller for a single-input single-output nonlinear system is designed from a new perspective. The proposed controller is model-free, that is, it is based on just input and output data. Therefore, it is suitable for systems with unknown models. The approach to design the controller is based on an optimization procedure. First, a linear regression estimation is assumed to exist for the system behavior. Then an optimal controller is designed for this estimated model. The cost function is proposed in a way that minimization of it, could guarantee that the sliding function and its first derivative converge to zero. Based on rigorous theoretical analysis, boundedness of the tracking error is then proved. Uncertainty is then considered and the control law is modified to cope with it. To demonstrate the validity and the performance of the proposed method in different situations, different computer simulations and experimental tests have been provided. Results show the effectiveness of the proposed method for different systems in different situations.
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering – SAGE
Published: Nov 1, 2018
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