International Journal of Control, Automation and Systems 2019; 17(1): 225-233
Published online January 3, 2019
https://doi.org/10.1007/s12555-018-0297-1
© The International Journal of Control, Automation, and Systems
This article considers a finite-time control problem of nonlinear quantized systems in complex environments. The controlled system is in a non-strict feedback form. By applying a nonlinear decomposition of hysteretic quantizer, the quantization issue is tackled successfully. By employing a structural property of radial basis function (RBF) neural networks (NNs), the conventional backstepping method is extended to non-strict feedback nonlinear quantized systems. Based on the finite time stability criterion, a new adaptive neural control scheme is presented. The constructed neural controller can ensure the transient performance of nonlinear quantized systems."
Keywords Adaptive control, finite-time control, neural network, quantized nonlinear systems.
International Journal of Control, Automation and Systems 2019; 17(1): 225-233
Published online January 1, 2019 https://doi.org/10.1007/s12555-018-0297-1
Copyright © The International Journal of Control, Automation, and Systems.
Xueyi Zhang, Fang Wang*, and Lili Zhang
Shandong University of Science and Technology
This article considers a finite-time control problem of nonlinear quantized systems in complex environments. The controlled system is in a non-strict feedback form. By applying a nonlinear decomposition of hysteretic quantizer, the quantization issue is tackled successfully. By employing a structural property of radial basis function (RBF) neural networks (NNs), the conventional backstepping method is extended to non-strict feedback nonlinear quantized systems. Based on the finite time stability criterion, a new adaptive neural control scheme is presented. The constructed neural controller can ensure the transient performance of nonlinear quantized systems."
Keywords: Adaptive control, finite-time control, neural network, quantized nonlinear systems.
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