A NARMAX model-based state-space self-tuning control for nonlinear stochastic hybrid systems

Applied Mathematical Modelling

Volumn 34, Issue 10, 2010, Pages 3030-3054

  Tsai, Jason Sheng Hong ^a   Wang, Chu Tong ^a   Kuang, Chi Chieh ^a   Guo, Shu Mei ^a   Shieh, Leang San ^b   Chen, Chia Wei ^c  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b University of Houston   (United States)

^c KAO YUAN UNIVERSITY   (Taiwan)

Author keywords

NARMAX model; Nonlinear control systems; Optimal linearization; State space self tuning control

Indexed keywords

ADVANCED CONTROL ALGORITHMS; CONTINUOUS TIME; DIGITAL CONTROL; ESTIMATED STATE; MULTI VARIABLES; NARMAX MODEL; NOISE MODELS; NON-LINEAR STOCHASTIC SYSTEMS; OPTIMAL LINEARIZATION; SELF TUNING CONTROLS; STATE-SPACE; STOCHASTIC HYBRID SYSTEMS; SYSTEM STATE;

ALGORITHMS; COMPOSITE STRUCTURES; CONTINUOUS TIME SYSTEMS; DIGITAL CONTROL SYSTEMS; ELECTRIC GROUNDING; HYBRID COMPUTERS; HYBRID SENSORS; HYBRID SYSTEMS; LINEARIZATION; NONLINEAR CONTROL SYSTEMS; OPTIMIZATION; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TUNING;

ADAPTIVE CONTROL SYSTEMS;

EID: 77952884777     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2010.01.011     Document Type: Article

References (26)

1. Leontaritis I.J., Billings S.A. Input-output parametric models for nonlinear systems, Parts I and II. Int. J. Control 1985, 41:303-344.
2. Zhu Q.M. An implicit least squares algorithm for nonlinear rational model parameter estimation. Appl. Math. Model. 2005, 29:673-689.
3. Sontag E.D. Polynomial Response Maps - Lecture Notes in Control and Identification Science 1979, vol. 13. Springer-Verlag, Berlin.
4. Warwick K. Self-tuning regulators - a state-space approach. Int. J. Control 1981, 33:839-858.
5. Tsay Y.T., Shieh L.S. State-space approach for self-tuning feedback control with pole assignment. IEE Proc. D, Control Theory Appl. 1981, 123:93-101.
6. Wang X.S., Cheng Y.H., Peng G.Z. Modeling and self-tuning pressure regulator design for pneumatic-pressure-load systems. Control Eng. Pract. 2007, 15:1161-1168.
7. Zhou M., Han F. Adaptive control for EDM process with a self-tuning regulator. Int. J. Mach. Tools Manuf. 2009, 49:462-469.
8. Ren T.J., Chen T.C., Chen C.J. Motion control for a two-wheeled vehicle using a self-tuning PID controller. Control Eng. Pract. 2008, 16:365-375.
9. Zheng J.M., Zhao S.D., Wei S.G. Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press. Control Eng. Pract. 2009, 17:1398-1404.
10. Äström K.J., Wittenmark B. Adaptive Control 1995, Addison-Wesley, Massachusetts. second ed.
11. Elliott H. Hybrid adaptive control of continuous-time systems. IEEE Trans. Automat. Contr. 1982, 27:419-426.
12. Gawthrop P.J. Hybrid self-tuning control. IEE Proc. D, Control Theory Appl. 1980, 127:229-236.
13. Narendra K., Khalifa I.H., Annaswamy A.M. Error models for stable hybrid adaptive systems. IEEE Trans. Automat. Contr. 1985, 30:339-347.
14. Bachelier O., Bosche J., Mehdi D. On pole placement via eigenstructure assignment approach. IEEE Trans. Automat. Contr. 2006, 51:1554-1558.
15. Wu J.L., Lee T.T. Optimal static output feedback simultaneous regional pole placement. IEEE Trans. Syst. Man Cybern. B Cybern. 2005, 35:881-893.
16. Lee S.H. Multirate digital control system design and its application to computer disk drives. IEEE Trans. Control Syst. Technol. 2006, 14:124-133.
17. Izadi I., Zhao Q., Chen T. An optimal scheme for fast rate fault detection based on multirate sampled data. J. Process Control 2005, 15:307-319.
18. Ioannou P., Fidan B. Adaptive Control Tutorial 2006, SIAM Press, Pennsylvania.
19. Heij C., Ran A., Schagen F.V. Introduction to Mathematical Systems Theory: Linear Systems. Identification and Control 2007, Birkhäuser Verlag, Basel-Boston-Berlin.
20. Zhang J., Ding F., Shi Y. Self-tuning control based on multi-innovation stochastic gradient parameter estimation. Syst. Control Lett. 2009, 58:69-75.
21. Chang Y.C., Yen H.M. Adaptive output feedback tracking control for a class of uncertain nonlinear systems using neural networks. IEEE Trans. Syst. Man Cybern. B Cybern. 2005, 35:1311-1316.
22. Kailath T. An innovation approach to least-squares estimation. Part I: Linear filtering in additive white noise. IEEE Trans. Automat. Contr. 1968, 13:646-655.
23. Dabney J.B., Harman T.L. Mastering Simulink 2004, Prentice-Hall, New Jersey.
24. Teixeira M.C.M., Zak S.H. Stabilizing controller design for uncertain nonlinear systems using fuzzy models. IEEE Trans. Fuzzy Syst. 1999, 7:133-142.
25. Shieh L.S., Tsay Y.T. Transformations of a class of multivariable control systems to block companion forms. IEEE Trans. Automat. Contr. 1982, 27:199-203.
26. C.C. Chuang, J.T. Jeng, T.T. Lee, Robust adaptive tracking control via CPBUM neural network for MIMO nonlinear systems, in: IEEE Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, vol. 7, 2004, pp. 4096-4101.