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Optimal control problem via self‐adaptation sliding mode controller with neural network
Author(s) -
Sakamoto Noriaki
Publication year - 2011
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.10390
Subject(s) - control theory (sociology) , artificial neural network , sliding mode control , controller (irrigation) , computer science , constant (computer programming) , optimal control , linear quadratic regulator , mode (computer interface) , control system , control engineering , control (management) , engineering , mathematics , nonlinear system , mathematical optimization , artificial intelligence , physics , quantum mechanics , operating system , electrical engineering , agronomy , biology , programming language
This paper proposes the author's new Self‐Adaptation Sliding Mode Controller which added a Neural Network (SA‐SMC+NN) for the optimal control problem. The controlled system is the linear time‐invariant system and the system parameter and the disturbance are unknown. In minimizing the quadratic cost function, the neural network gives the coefficients of the switching function of the sliding mode control. According to this proposed technique, we do not have to tune the parameters of the controller when applying SA‐SMC+NN. Furthermore, we are able to get constant feedback gain such as the optimal control regulator for an uncertain system based on the control results by SA‐SMC+NN. A differential game is simulated to confirm the effectiveness of the proposed method. © 2011 Wiley Periodicals, Inc. Electron Comm Jpn, 94(11): 1–8, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/ecj.10390