TY - GEN
T1 - An adaptive predictive control based on a quasi-ARX neural network model
AU - Jami'In, Mohammad Abu
AU - Sutrisno, Imam
AU - Hu, Jinglu
AU - Mariun, Norman Bin
AU - Marhaban, Mohd Hamiruce
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - A quasi-ARX (quasi-linear ARX) neural network (QARXNN) model is able to demonstrate its ability for identification and prediction highly nonlinear system. The model is simplified by a linear correlation between the input vector and its nonlinear coefficients. The coefficients are used to parameterize the input vector performed by an embedded system called as state dependent parameter estimation (SDPE), which is executed by multi layer parceptron neural network (MLPNN). SDPE consists of the linear and nonlinear parts. The controller law is derived via SDPE of the linear and nonlinear parts through switching mechanism. The dynamic tracking controller error is derived then the stability analysis of the closed-loop controller is performed based Lyapunov theorem. Linear based adaptive robust control and nonlinear based adaptive robust control is performed with the switching of the linear and nonlinear parts parameters based Lyapunov theorem to guarantee bounded and convergence error.
AB - A quasi-ARX (quasi-linear ARX) neural network (QARXNN) model is able to demonstrate its ability for identification and prediction highly nonlinear system. The model is simplified by a linear correlation between the input vector and its nonlinear coefficients. The coefficients are used to parameterize the input vector performed by an embedded system called as state dependent parameter estimation (SDPE), which is executed by multi layer parceptron neural network (MLPNN). SDPE consists of the linear and nonlinear parts. The controller law is derived via SDPE of the linear and nonlinear parts through switching mechanism. The dynamic tracking controller error is derived then the stability analysis of the closed-loop controller is performed based Lyapunov theorem. Linear based adaptive robust control and nonlinear based adaptive robust control is performed with the switching of the linear and nonlinear parts parameters based Lyapunov theorem to guarantee bounded and convergence error.
UR - http://www.scopus.com/inward/record.url?scp=84949925542&partnerID=8YFLogxK
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U2 - 10.1109/ICARCV.2014.7064314
DO - 10.1109/ICARCV.2014.7064314
M3 - Conference contribution
AN - SCOPUS:84949925542
T3 - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
SP - 253
EP - 258
BT - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Y2 - 10 December 2014 through 12 December 2014
ER -