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An adaptive predictive control based on a quasi-ARX neural network model


Citation

Abu Jami’in, Mohammad and Sutrisno, Imam and Hu, Jinglu and Mariun, Norman and Marhaban, Mohammad Hamiruce (2014) An adaptive predictive control based on a quasi-ARX neural network model. In: 13th International Conference on Control Automation Robotics & Vision (ICARCV 2014), 10-12 Dec. 2014 , Marina Bay Sands, Singapore. (pp. 253-258).

Abstract / Synopsis

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.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
DOI Number: 10.1109/ICARCV.2014.7064314
Publisher: IEEE
Keywords: Adaptation model; Autoregressive processes; Nonlinear systems; Predictive models
Depositing User: Azian Edawati Zakaria
Date Deposited: 20 Nov 2015 15:14
Last Modified: 28 Jan 2016 10:29
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICARCV.2014.7064314
URI: http://psasir.upm.edu.my/id/eprint/41438
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