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Optimal sliding‐mode control of linear systems with uncertainties and input constraints using projection neural network
Author(s) -
Toshani Hamid,
Farrokhi Mohammad
Publication year - 2017
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2385
Subject(s) - control theory (sociology) , robustness (evolution) , artificial neural network , sliding mode control , projection method , computer science , actuator , optimal control , projection (relational algebra) , dykstra's projection algorithm , mathematical optimization , mathematics , nonlinear system , algorithm , control (management) , artificial intelligence , biochemistry , chemistry , physics , quantum mechanics , gene
Summary 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.