Regular Papers

International Journal of Control, Automation and Systems 2020; 18(12): 3101-3111

Published online June 24, 2020

https://doi.org/10.1007/s12555-019-0029-1

© The International Journal of Control, Automation, and Systems

Neuro-based Canonical Transformation of Port Controlled Hamiltonian Systems

Aminuddin Qureshi, Sami El Ferik*, and Frank L. Lewis

King Fahd University of Petroleum and Minerals

Abstract

In the literature of control theory, tracking control of port controlled Hamiltonian systems is generally achieved using canonical transformation. Closed form evaluation of state-feedback for the canonical transformation requires the solution of certain partial differential equations which becomes very difficult for nonlinear systems. This paper presents the application of neural networks for the canonical transformation of port controlled Hamiltonian systems. Instead of solving the partial differential equations, neural networks are used to approximate the closedform state-feedback required for canonical transformation. Ultimate boundedness of the tracking and neural network weight errors is guaranteed. The proposed approach is structure preserving. The application of neural networks is direct and off-line processing of neural networks is not needed. Efficacy of the proposed approach is demonstrated with the examples of a mass-spring system, a two-link robot arm and an Autonomous Underwater Vehicle (AUV).

Download: http://link.springer.com/article/10.1007/s12555-019-0029-1

Keywords Canonical transformation, L2 disturbance attenuation, neural networks, port controlled Hamiltonian systems.

Article

Regular Papers

International Journal of Control, Automation and Systems 2020; 18(12): 3101-3111

Published online December 1, 2020 https://doi.org/10.1007/s12555-019-0029-1

Copyright © The International Journal of Control, Automation, and Systems.

Neuro-based Canonical Transformation of Port Controlled Hamiltonian Systems

Aminuddin Qureshi, Sami El Ferik*, and Frank L. Lewis

King Fahd University of Petroleum and Minerals

Abstract

In the literature of control theory, tracking control of port controlled Hamiltonian systems is generally achieved using canonical transformation. Closed form evaluation of state-feedback for the canonical transformation requires the solution of certain partial differential equations which becomes very difficult for nonlinear systems. This paper presents the application of neural networks for the canonical transformation of port controlled Hamiltonian systems. Instead of solving the partial differential equations, neural networks are used to approximate the closedform state-feedback required for canonical transformation. Ultimate boundedness of the tracking and neural network weight errors is guaranteed. The proposed approach is structure preserving. The application of neural networks is direct and off-line processing of neural networks is not needed. Efficacy of the proposed approach is demonstrated with the examples of a mass-spring system, a two-link robot arm and an Autonomous Underwater Vehicle (AUV).

Download: http://link.springer.com/article/10.1007/s12555-019-0029-1

Keywords: Canonical transformation, L2 disturbance attenuation, neural networks, port controlled Hamiltonian systems.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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