Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems

Hussain, Mohd Azlan and Ho, P.Y. (2004) Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems. Journal of the Chinese Institute of Chemical Engineers, 35 (3). pp. 353-362. ISSN 0368-1653,

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The difficulties associated with the control of nonlinear systems are especially profound when it involves MIMO systems. One possible approach to tackle the system nonlinearities is to employ the input-output feedback linearizing control strategy. However, this controller can only perform well when the exact knowledge of the system is known. To alleviate this problem, it is proposed here to use neural-network-based hybrid models to model the system nonlinear functions. Particularly, multilayer feedforward networks are used to model the unknown parts of the system nonlinear functions, and then the network outputs are combined with the available knowledge to form the hybrid models. Simulation studies are shown on set point tracking and disturbance rejection studies of two continuous stirred tank reactors, one with single reaction, and another one with multiple reactions. The results showed that the control systems were able to track the set points and reject disturbances with only slight overshoot during the transient period.

Item Type: Article
Additional Information: 842LD Times Cited:0 Cited References Count:16
Uncontrolled Keywords: Adaptive control; Hybrid models; MIMO nonlinear system; Neural networks; Adaptive control systems; Computer simulation; Feedback control; Functions; Mathematical models; Multilayer neural networks; MIMO systems; Neural network based hybrid models; Nonlinear functions; Tank reactors; Nonlinear systems; process control.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 06:34
Last Modified: 10 Feb 2021 03:44

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