TY - JOUR
T1 - An improved adaptive switching control based on quasi-ARX neural network for nonlinear systems
AU - Sutrisno, Imam
AU - Che, Chi
AU - Hu, Jinglu
N1 - Publisher Copyright:
© 2014, ISAROB.
PY - 2014/12/10
Y1 - 2014/12/10
N2 - In this paper, an improved switching mechanism based on quasi-linear auto regressive exogenous (quasi-ARX) neural network (QARXNN) is presented for the adaptive control of nonlinear systems. The proposed switching control is composed of a QARXNN-based prediction model and an improved switching mechanism using two new adaptive control laws, first is moving average filter law and second is new switching law. Since the control result of nonlinear predictor is better than the linear predictor in most of the time, the adaptive control with a simple switching mechanism has many useless switching during the processing. Hence, the improved smooth switching mechanism is proposed to replace the original switching mechanism; it can improve the performance by reducing the useless switching while guaranteeing stability of the system control. The simulations show that the efficiency of the proposed control method is satisfied in stability, improve control accuracy and robustness.
AB - In this paper, an improved switching mechanism based on quasi-linear auto regressive exogenous (quasi-ARX) neural network (QARXNN) is presented for the adaptive control of nonlinear systems. The proposed switching control is composed of a QARXNN-based prediction model and an improved switching mechanism using two new adaptive control laws, first is moving average filter law and second is new switching law. Since the control result of nonlinear predictor is better than the linear predictor in most of the time, the adaptive control with a simple switching mechanism has many useless switching during the processing. Hence, the improved smooth switching mechanism is proposed to replace the original switching mechanism; it can improve the performance by reducing the useless switching while guaranteeing stability of the system control. The simulations show that the efficiency of the proposed control method is satisfied in stability, improve control accuracy and robustness.
KW - Adaptive switching control
KW - An improved switching mechanism
KW - Quasi-ARX neural network prediction model
UR - http://www.scopus.com/inward/record.url?scp=84919390926&partnerID=8YFLogxK
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U2 - 10.1007/s10015-014-0173-x
DO - 10.1007/s10015-014-0173-x
M3 - Article
AN - SCOPUS:84919390926
VL - 19
SP - 347
EP - 353
JO - Artificial Life and Robotics
JF - Artificial Life and Robotics
SN - 1433-5298
IS - 4
ER -