ℒ2 neuro-adaptive tracking control of uncertain port-controlled Hamiltonian systems
- Author(s): Aminuddin Qureshi 1 ; Sami El Ferik 1 ; Frank L. Lewis 2
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View affiliations
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Affiliations:
1:
Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Kingdom of Saudi Arabia;
2: Research Institute, The University of Texas at Arlington, Texas 76118, USA
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Affiliations:
1:
Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Kingdom of Saudi Arabia;
- Source:
Volume 9, Issue 12,
06 August 2015,
p.
1781 – 1790
DOI: 10.1049/iet-cta.2014.1144 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received
14/06/2014,
Accepted
23/03/2015,
Revised
09/02/2015,
Published
16/06/2015
This study presents a practical method of neural network (NN) adaptive tracking control of uncertain port-controlled Hamiltonian (PCH) systems. NN is used to compensate for parametric uncertainties and unlike the previous studies, the dynamics of the NN tuning law is driven by both the position as well as the velocity errors owing to the introduction of the information preserving filtering of the Hamiltonian gradient. In addition, the proposed controller achieves the ℒ2 disturbance attenuation objectives as well as preserves the PCH structure of the system in closed loop. Simulation examples demonstrate the efficacy of the proposed approach.
Inspec keywords: uncertain systems; adaptive control; neurocontrollers
Other keywords: Hamiltonian gradient; neuro-adaptive tracking control; NN tuning law; uncertain port-controlled Hamiltonian systems; PCH systems; NN adaptive tracking control; neural network; PCH structure; velocity errors
Subjects: Neurocontrol; Self-adjusting control systems
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