Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems
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...
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Journal of the Chinese Institute of Chemical Engineers
2004
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my.um.eprints.70632021-02-10T03:44:28Z http://eprints.um.edu.my/7063/ Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems Hussain, Mohd Azlan Ho, P.Y. TA Engineering (General). Civil engineering (General) TP Chemical technology 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. Journal of the Chinese Institute of Chemical Engineers 2004 Article PeerReviewed 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 http://www.scopus.com/inward/record.url?eid=2-s2.0-3543081525&partnerID=40&md5=80e7b1fdf507e64dbba89cb0dee5d1cd |
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TA Engineering (General). Civil engineering (General) TP Chemical technology Hussain, Mohd Azlan Ho, P.Y. Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems |
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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. |
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Article |
author |
Hussain, Mohd Azlan Ho, P.Y. |
author_facet |
Hussain, Mohd Azlan Ho, P.Y. |
author_sort |
Hussain, Mohd Azlan |
title |
Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems |
title_short |
Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems |
title_full |
Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems |
title_fullStr |
Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems |
title_full_unstemmed |
Adaptive feedback linearizing control with neural-network-based hybrid models for MIMO nonlinear systems |
title_sort |
adaptive feedback linearizing control with neural-network-based hybrid models for mimo nonlinear systems |
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Journal of the Chinese Institute of Chemical Engineers |
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2004 |
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http://eprints.um.edu.my/7063/ http://www.scopus.com/inward/record.url?eid=2-s2.0-3543081525&partnerID=40&md5=80e7b1fdf507e64dbba89cb0dee5d1cd |
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13.160551 |