Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems.

Mehrafrooz, A; He, F; Lalbakhsh, A

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems.

Mehrafrooz, A He, F Lalbakhsh, A

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2022, 22, (6), pp. 1-22
Issue Date:: 2022-03-08

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Field	Value	Language
dc.contributor.author	Mehrafrooz, A
dc.contributor.author	He, F
dc.contributor.author	Lalbakhsh, A
dc.date.accessioned	2023-07-02T23:20:00Z
dc.date.available	2022-03-07
dc.date.available	2023-07-02T23:20:00Z
dc.date.issued	2022-03-08
dc.identifier.citation	Sensors (Basel), 2022, 22, (6), pp. 1-22
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/171109
dc.description.abstract	In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a 'black box' with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights' adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s22062089
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Models, Biological
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Nonlinear Dynamics
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Models, Biological
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Nonlinear Dynamics
dc.subject.mesh	Algorithms
dc.subject.mesh	Nonlinear Dynamics
dc.subject.mesh	Models, Biological
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Models, Biological
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Nonlinear Dynamics
dc.title	Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems.
dc.type	Journal Article
utslib.citation.volume	22
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2023-07-02T23:19:55Z
pubs.issue	6
pubs.publication-status	Published online
pubs.volume	22
utslib.citation.issue	6

Abstract:

In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a 'black box' with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights' adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.

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