Optimal sliding‐mode control of linear systems with uncertainties and input constraints using projection neural network

Optimal sliding‐mode control of linear systems with uncertainties and input constraints using... In this paper, an optimal sliding‐mode control (SMC) method based on the projection recurrent neural networks for a class of linear systems with uncertainties and input constraint is developed. The chattering in the SMC is eliminated by introducing a performance index for minimizing the sliding‐surface variations and the control effort. Moreover, the constraints on the actuators are considered in the optimization problem, which is solved using projection recurrent neural network. The main advantages of the proposed method are obtaining an optimal and chattering‐free control law in a feasible space. Moreover, the parameters of the proposed control method are determined based on the closed‐loop stability and robustness analysis. The performance of the proposed method is evaluated by considering uncertainties and input constraints in the system and is compared with the conventional and a second‐order SMC in the view of chattering, input saturation, and robustness. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optimal Control Applications and Methods Wiley

Optimal sliding‐mode control of linear systems with uncertainties and input constraints using projection neural network

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
0143-2087
eISSN
1099-1514
D.O.I.
10.1002/oca.2385
Publisher site
See Article on Publisher Site

Abstract

In this paper, an optimal sliding‐mode control (SMC) method based on the projection recurrent neural networks for a class of linear systems with uncertainties and input constraint is developed. The chattering in the SMC is eliminated by introducing a performance index for minimizing the sliding‐surface variations and the control effort. Moreover, the constraints on the actuators are considered in the optimization problem, which is solved using projection recurrent neural network. The main advantages of the proposed method are obtaining an optimal and chattering‐free control law in a feasible space. Moreover, the parameters of the proposed control method are determined based on the closed‐loop stability and robustness analysis. The performance of the proposed method is evaluated by considering uncertainties and input constraints in the system and is compared with the conventional and a second‐order SMC in the view of chattering, input saturation, and robustness.

Journal

Optimal Control Applications and MethodsWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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