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Approximate adaptive output feedback stabilization via passivation of MIMO uncertain systems using neural networks.
IEEE Trans Syst Man Cybern B Cybern. 2009 Oct; 39(5):1180-91.IT

Abstract

An adaptive output feedback neural network controller is designed, which is capable of rendering affine-in-the-control uncertain multi-input-multi-output nonlinear systems strictly passive with respect to an appropriately defined set. Consequently, a simple output feedback is employed to stabilize the system. The controlled system need not be in normal form or have a well-defined relative degree. Without requiring a zero-state detectability assumption, uniform ultimate boundedness, with respect to an arbitrarily small set, of both the system's state and the output is guaranteed, along with boundedness of all other signals in the closed loop. To effectively avoid possible division by zero, the proposed adaptive controller is of switching type. However, its continuity is guaranteed, thus alleviating drawbacks connected to existence of solutions and chattering phenomena. Simulations illustrate the approach.

Authors+Show Affiliations

Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.No affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

19336316

Citation

Kostarigka, Artemis K., and George A. Rovithakis. "Approximate Adaptive Output Feedback Stabilization Via Passivation of MIMO Uncertain Systems Using Neural Networks." IEEE Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society, vol. 39, no. 5, 2009, pp. 1180-91.
Kostarigka AK, Rovithakis GA. Approximate adaptive output feedback stabilization via passivation of MIMO uncertain systems using neural networks. IEEE Trans Syst Man Cybern B Cybern. 2009;39(5):1180-91.
Kostarigka, A. K., & Rovithakis, G. A. (2009). Approximate adaptive output feedback stabilization via passivation of MIMO uncertain systems using neural networks. IEEE Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society, 39(5), 1180-91. https://doi.org/10.1109/TSMCB.2009.2013477
Kostarigka AK, Rovithakis GA. Approximate Adaptive Output Feedback Stabilization Via Passivation of MIMO Uncertain Systems Using Neural Networks. IEEE Trans Syst Man Cybern B Cybern. 2009;39(5):1180-91. PubMed PMID: 19336316.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Approximate adaptive output feedback stabilization via passivation of MIMO uncertain systems using neural networks. AU - Kostarigka,Artemis K, AU - Rovithakis,George A, Y1 - 2009/03/24/ PY - 2009/4/2/entrez PY - 2009/4/2/pubmed PY - 2009/7/25/medline SP - 1180 EP - 91 JF - IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society JO - IEEE Trans Syst Man Cybern B Cybern VL - 39 IS - 5 N2 - An adaptive output feedback neural network controller is designed, which is capable of rendering affine-in-the-control uncertain multi-input-multi-output nonlinear systems strictly passive with respect to an appropriately defined set. Consequently, a simple output feedback is employed to stabilize the system. The controlled system need not be in normal form or have a well-defined relative degree. Without requiring a zero-state detectability assumption, uniform ultimate boundedness, with respect to an arbitrarily small set, of both the system's state and the output is guaranteed, along with boundedness of all other signals in the closed loop. To effectively avoid possible division by zero, the proposed adaptive controller is of switching type. However, its continuity is guaranteed, thus alleviating drawbacks connected to existence of solutions and chattering phenomena. Simulations illustrate the approach. SN - 1941-0492 UR - https://www.unboundmedicine.com/medline/citation/19336316/Approximate_adaptive_output_feedback_stabilization_via_passivation_of_MIMO_uncertain_systems_using_neural_networks_ L2 - https://dx.doi.org/10.1109/TSMCB.2009.2013477 DB - PRIME DP - Unbound Medicine ER -