Adaptive control system having direct output feedback and...

Data processing: artificial intelligence – Neural network – Learning task

Reexamination Certificate

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C706S013000

Reexamination Certificate

active

06904422

ABSTRACT:
An adaptive control system (ACS) uses direct output feedback to control a plant. The ACS uses direct adaptive output feedback control developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded.

REFERENCES:
patent: 5049796 (1991-09-01), Seraji
patent: 5121467 (1992-06-01), Skeirik
patent: 5142612 (1992-08-01), Skeirik
patent: 5167009 (1992-11-01), Skeirik
patent: 5197114 (1993-03-01), Skeirik
patent: 5224203 (1993-06-01), Skeirik
patent: 5367612 (1994-11-01), Bozich et al.
patent: 5426720 (1995-06-01), Bozich et al.
patent: 5493631 (1996-02-01), Huang et al.
patent: 5586221 (1996-12-01), Isik et al.
patent: 5680513 (1997-10-01), Hyland et al.
patent: 5796920 (1998-08-01), Hyland
patent: 5796922 (1998-08-01), Smith
patent: 5943660 (1999-08-01), Yesildirek et al.
patent: 5959861 (1999-09-01), Kaneko
patent: 6055524 (2000-04-01), Cheng
patent: 6064997 (2000-05-01), Jagannathan et al.
patent: 6085183 (2000-07-01), Horn et al.
patent: 6351740 (2002-02-01), Rabinowitz
patent: 6532454 (2003-03-01), Werbos
patent: 6611823 (2003-08-01), Selmic et al.
Chen et al., “Adaptive Control of a Class of Nonlinear Systems using Neural Networks”, Proceedings of the 34th Conference o Decision and Control, Dec. 1995.
McFarland et al., “Robustness Analysis for a Neural Network Based Adaptive Control Scheme”, Proceedings of the American Control Conference, Jun. 1995.
Calise et al., “Design of Optimal Output Feedback Compensators in Two-Time Scale Systems”, IEEE Transactions on Automa Control, Apr. 1990.
Moerder et al., “Near-Optimal Output Feedback Regulation of Ill-Conditioned Linear Systems”, IEEE Transactions on Automati Control, May 1988.
Rysdyk et al., “Robust Adaptive Nonlinear Flight Control Applications Using Neural Networks”, Proceedings of the American Control Conference, Jun. 1999.
McFarland et al., “Robust Adaptive Control Using Single-Hidden-Layer Feedforward Neural Networks”, Proceedings of the American Control Conference, Jun. 1999.
Calise et al., “An SPR Approach for Adaptive Output Feedback Control with Neural Networks”, Proceedings of the 40th IEEE Conference on Decision and Control, Dec. 2001.
Brdys et al., “Recurrent Networks for Nonlinear Adaptive Control”, IEE Proceedings-Control Theory and Applications, Mar. 1998.
Widrow et al., “Adaptive Inverse Control Based on Linear and Nonlinear Adaptive Filtering”, Proceedings of the International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing, Aug. 1996.
Chen et al., “Adaptive Control of a Class of Nonlinear Discrete-Time Systems Using Neural Networks”, IEEE Transactions on Automatic Control, May 1995.
Hussain et al., “Nonlinear Control With Linearised Models and Neural Networks”, 4th International Conference on Artificial Neural Networks, Jun. 1995.
Hovakimyan et al., “Dynamic Neural Networks for Output Feedback Control”, Proceedings of the 38th Conference on Decision and Control, Dec. 1999.
Yamada et al., “Remarks on an Adaptive Type Self-Tuning Controller Using Neural Networks”, 1991 International Conference o Industrial Electronics, Control and Instrumentation, 1991.
Anthony J. Calise, et al., Adaptive output feedback control of nonlinear systems using neural networks, Automatica, Mar. 7, 2001, pp. 1201-1211, Automatica 37, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Young H. Kim, et al., A Dynamic Recurrent Neural-network-based Adaptive Observer for a Class of Nonlinear Systems, Feb. 18, 1997, pp. 1539-1543, Automatica, vol. 33, No. 8, The University of Texas at Arlington, Fort Worth, Texas & The University of New Mexico, Albuquerque, NM, USA.
F.L. Lewis, Nonlinear Network Structures for Feedback Control, Asian Journal of Control, Dec. 1999, pp. 205-228, vol. 1, No. 4.
Moshe Idan, et al., A Hierarchical Approach to Adaptive Control for Improved Flight Safety, Copyright 2001, pp. 1-11, American Institute of Aeronautics and Astronautics, Reston, VA.
Anthony J. Calise, et al., Development of a Reconfigurable Flight Control Law for the X-36 Tailless Fighter Aircraft, AIAA 2000-3940 Copyright 2000, pp. 1-9, American Institute of Aeronautics and Astronautics, Reston, VA.
Manu Sharma, et al., Neural Network Augmentation of Existing Linear Controllers, AIAA 2001-4163, Copyright 2001, pp. 1-9, American Institute of Aeronautics and Astronautics, Reston, VA.
Naira Hovakimyan, et al. Adaptive Output Feedback Control of a Class of Nonlinear Systems Using Neural Networks, Apr. 23, 2001, pp. 1-21, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA.
Ken-ichi Funahashi, On the Approximate Realization of Continuous Mappings by Neural Networks, Sep. 14, 1988, pp. 183-192, vol. 2, Neural Networks, USA.
G. Cybenko, Approximation by Superpositions of a Sigmoidal Function, Mathematics of Control, Signals, and Systems, Feb. 17, 1989, pp. 303-315, vol. 2, Springer-Verlag New York Inc., USA.
Robert M. Sanner, et al., Gaussian Networks for Direct Adaptive Control, Nov., 1992, pp. 837-863, vol. 3, No. 6, IEEE Transactions on Neural Networks.
Sridhar Seshagiri, et al., Output Feedback Control of Nonlinear Systems Using RBF Neural Networks, Jan., 2000, pp. 69-79, vol. II, No. 1, IEEE Transactions on Neural Network.
Jin Young Choi, et al., Observer-based Backstepping Control Using On-line Approximation, Proceedings of the American Control Conference, Chicago, Illinois, Jun. 2000, pp. 3646-3650.
Kurt Hornik, Multilayer Feedforward Networks are Universal Approximators, Neural Networks, Copyright 1989, pp. 359-366, vol. 2, USA.
J. Horn: “Feedback-Linearization using Neural Process Models” Proceedings of the 9th International Conference on Artificial Neural Networks, vol. 1, Sep. 7, 1999, pp. 37-42, XP001059447 UK, p. 37, right-hand column, line 1-p. 38, right-hand column, line 3.
Y. Zhang et al.: “Stable Neural Controller Design for Unknown Nonlinear Systems Using Backstepping” Proceedings of the 1999 American Control Conference, vol. 2, Jun. 2, 1999, pp. 1067-1071, XP010344848, USA, p. 1067, left-hand column, line 60-p. 1068, right-hand column, line 37.
K. Hasibi: “Implementation of Feedback Linearizable Controllers” Proceedings of the Spie, vol. 1709, 1992, pp. 541-547, XP001059643 USA, p. 541, line 45-p. 542, line 32.
I. Egemen et al.: “Disturbance Attenuating Adaptive Controllers for Parameteric Strict Feedback Nonlinear Systems with Output Measurements” Transactions of the Asme, vol. 121, No. 1, Mar. 1999, pp. 48-57, XP001059617 USA, p. 49, left-hand column, line 46-p. 50, left-hand column, line 57.
Podsiadlo P. et al.: “Output Feedback Adaptive Control of Non-Affine MIMO Systems” Motion Control Proceedings, 1993, Asia-Pacific Workshop on Advances in Singapore Jul. 15-16, 1993, New York, NY, USA, IEEE, pp. 159-164, XP010113424, ISBN: 0-7803-1223-6 the whole document.
Patent Cooperation Treaty, Invitation to Pay Additional Fees with Annex to Form PCT/ISA/206 Communication Relating to the Results of the Partial International Search, Feb. 15, 2002, International Application No. PCT/US01/17201, filed May 25, 2001 by applicant Georgia Tech Research Corporation.
PCT Written Opinion, PCT/US01/17201 (PCT application related to present application) IPEA/EPO mailed Jul. 19, 2002.
PCT International Search Report, PCT/US01/172

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