Nonlinear and robust model predictive control of systems with unmodeled dynamics based on supervised learning and neurodynamic optimization

Yan, Z; Wang, J

Nonlinear and robust model predictive control of systems with unmodeled dynamics based on supervised learning and neurodynamic optimization

Yan, Z

Wang, J

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Publication Type:: Chapter
Citation:: Frontiers of Intelligent Control and Information Processing, 2014, pp. 193 - 220
Issue Date:: 2014-08-13

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Field	Value	Language
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2014-08-13	en_US
dc.identifier.citation	Frontiers of Intelligent Control and Information Processing, 2014, pp. 193 - 220	en_US
dc.identifier.isbn	9789814616874	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119758
dc.description.abstract	© 2015 by World Scientific Publishing Co. Pte. Ltd. Model predictive control is an optimization-based control strategy which has achieved enormous successes in numerous real-world applications. MPC generates control signals by means of real-time optimization of a performance index over a finite moving horizon of predicted future, subject to system constraints. A major challenge of the MPC research and development lies in the realization of nonlinear and robust MPC approaches, especially to cases where unmodeled dynamics exist. This chapter presents novel MPC approaches to nonlinear systems with unmodeled dynamics based on neural networks. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The linearization residues, together with the unmodeled dynamics, are then modeled by using a feedforward neural network called the Extreme Learning Machine via supervised learning. When the controlled system is affected by bounded additive disturbances, the minimax methodology is exploited to achieve robustness. The nonlinear and robust MPC problems are formulated as constrained convex optimization problems and iteratively solved by applying neurodynamic optimization approaches. The applied neurodynamic optimization approaches can compute the optimal control signals in real-time, which shed a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approaches.	en_US
dc.relation.ispartof	Frontiers of Intelligent Control and Information Processing	en_US
dc.relation.isbasedon	10.1142/9789814616881_0007	en_US
dc.title	Nonlinear and robust model predictive control of systems with unmodeled dynamics based on supervised learning and neurodynamic optimization	en_US
dc.type	Chapter
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 by World Scientific Publishing Co. Pte. Ltd. Model predictive control is an optimization-based control strategy which has achieved enormous successes in numerous real-world applications. MPC generates control signals by means of real-time optimization of a performance index over a finite moving horizon of predicted future, subject to system constraints. A major challenge of the MPC research and development lies in the realization of nonlinear and robust MPC approaches, especially to cases where unmodeled dynamics exist. This chapter presents novel MPC approaches to nonlinear systems with unmodeled dynamics based on neural networks. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The linearization residues, together with the unmodeled dynamics, are then modeled by using a feedforward neural network called the Extreme Learning Machine via supervised learning. When the controlled system is affected by bounded additive disturbances, the minimax methodology is exploited to achieve robustness. The nonlinear and robust MPC problems are formulated as constrained convex optimization problems and iteratively solved by applying neurodynamic optimization approaches. The applied neurodynamic optimization approaches can compute the optimal control signals in real-time, which shed a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approaches.

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http://hdl.handle.net/10453/119758