Abstract
This article focuses on an adaptive neural network (NN) control problem for multi-input multi-output nonstrict-feedback nonlinear systems subject to unmeasurable states and actuator hysteresis. Firstly, a NN observer is proposed to obtain the unmeasurable states. In addition, the radial basis function neural networks are applied to online approximate the nonlinear terms. And then, a variable separation technique is utilized to solve the algebraic loop problem generated by the nonstrict-feedback structure and the complexity problem is also dealt with by using a command filter design technique, which is easy to reduce the repeated differentiations of virtual control signals. Meanwhile, to satisfy the practical engineering application, a finite-time performance function is implemented to make the tracking error can enter into the preassigned range within a given time. By using the proposed controller, the boundedness of all closed-loop signals is guaranteed and the tracking error can enter into the prescribed bounded range within a precise setting time. Finally, the preponderance and effectiveness of the developed controller are revealed by simulation examples.
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This work was partly supported byNational Natural Science Foundation of China (62122046), (61973204), (52101346).
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Liu, X., Wu, Y., Wu, N. et al. Finite-time-prescribed performance-based adaptive command filtering control for MIMO nonlinear systems with unknown hysteresis. Nonlinear Dyn 111, 7357–7375 (2023). https://doi.org/10.1007/s11071-022-08216-6
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DOI: https://doi.org/10.1007/s11071-022-08216-6