Quasi-ARX Neural Network Based Adaptive Predictive Control for Nonlinear Systems

Abu Jami’in, Mohammad and Hu, Jinglu and Hamiruce Marhaban, Mohammad and Sutrisno, Imam and Bin Mariun, Norman (2016) Quasi-ARX Neural Network Based Adaptive Predictive Control for Nonlinear Systems. IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING (11). pp. 83-90.

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Abstract

In this paper, a new switching mechanism is proposed based on the state of dynamic tracking error so that more information will be provided –not only the error but also a one up to pth differential error will be available as the switching variable. The switching index is based on the Lyapunov stability theory. Thus the switching mechanism can work more effectively and efficiently. A simplified quasi-ARX neural-network (QARXNN) model presented by a state-dependent parameter estimation (SDPE) is used to derive the controller formulation to deal with its computational complexity. The switching works inside the model by utilizing the linear and nonlinear parts of an SDPE. First, a QARXNN is used as an estimator to estimate an SDPE. Second, by using SDPE, the state of dynamic tracking error is calculated to derive the switching index. Additionally, the switching formula can use an SDPE as the switching variable more easily. Finally, numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance-rejection performances. Experimental results demonstrate its effectiveness. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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