Browse > Article

Adaptive Predictive Control using Multiple Models, Switching and Tuning  

Giovanini Leonardo (Industrial Control Centre, University of Strathclyde)
Ordys Andrzej W. (Kingston University)
Grimble Michael J. (Industrial Control Centre, University of Strathclyde)
Publication Information
International Journal of Control, Automation, and Systems / v.4, no.6, 2006 , pp. 669-681 More about this Journal
Abstract
In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.
Keywords
Adaptive control; infinite controller cover set; multiple models; multi-objective optimization; predictive control;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By Web Of Science : 5  (Related Records In Web of Science)
Times Cited By SCOPUS : 5
연도 인용수 순위
1 K. Fruzzetti, A. Palazoglu, and K. Mac Donald, 'Nonlinear model predictive control using Hammerstein models,' Journal of Process Control, vol. 7, no. 1, pp. 31-41, 1997   DOI   ScienceOn
2 H. Genceli and M. Nikolaou, 'Design of robust constrained model predictive controllers with Volterra series,' AIChE J, vol. 41, no. 10, pp. 2098-2107, 1995   DOI   ScienceOn
3 B. Anderson, T. Brinsmead, F. de Bruyne, J. Hespanha, D. Liberzon, and S. Morse, 'Multiple model adaptive control I: Finite controller coverings,' International Journal of Robust and Nonlinear Control, vol. 10, no. 9, pp. 909-929, 2000   DOI   ScienceOn
4 K. Narendra and J Balakrishnan, 'Adaptive control using multiple models,' IEEE Trans. on Automatic Control, vol. 42, no. 2, pp. 171-187, 1997   DOI   ScienceOn
5 F. Bianchini and M. Snaizer, 'A convex optimization approach for fixed-order controller design for disturbance rejection in SISO systems,' IEEE Trans. on Automatic Control, vol. 45, pp. 784-789,2000   DOI   ScienceOn
6 E. Gilbert, and I. Kolmanovsky, 'Maximal output admissible sets for discrete-time systems with disturbance inputs,' Proc. of the American Contr. Conf, pp. 2000-2005, 1995
7 W. Zhuang, 'RLS algorithm with variable forgetting factor for decision feedback equalizer over time-variant fading channels,' Wireless Personal Communications, vol. 8, no. 1, pp. 1529, 1998
8 M. Campi, J. Hespanha, and M. Prandini, 'Cautious hierarchical switching control of stochastic linear systems,' International Journal of Adaptive Control and Signal Processing, vol. 18, no. 4,pp. 319-333,2004   DOI   ScienceOn
9 J. Momingred, B. Paden, D. Seborg, and D. Mellichamp, 'An adaptive nonlinear predictive controller,' Chem. Eng. Sci., vol. 47, no. 4, pp. 755-765, 1992   DOI   ScienceOn
10 S. Morse, 'Supervisory control of families of linear set-point controllers-Part 1: Exact matching,' IEEE Trans. on Automatic Control, vol. 41, no. 8, pp. 1413-1431,1996   DOI   ScienceOn
11 A. Rantzer and M. Johansson, 'Piecewise linear quadratic optimal control,' IEEE Trans. on Automatic Control, vol. 45, no. 4, pp. 629-637, 2000   DOI   ScienceOn
12 J. Norquay, A. Palazoglu, and J. Romagnoli, 'Model predictive control based on Weiner models,' Chemical Engineering Science, vol. 53, pp. 75-84, 1998   DOI   ScienceOn
13 C. Desoer and M. Vidyasagar, Feedback System: Input-Output Properties, Academic Press, 1975
14 B. Polyak and P. Scherbakov, 'Superstable Linear Control Systems I: Analysis,' Autom. and Remote Control, vol. 63, no. 8, pp. 1-16,2002   DOI
15 B. Polyak and M. Halpern, 'Optimal design for discrete-time linear systems via new performance index,' Adaptive Control and Signal Processing, vol. 15, no. 2, pp. 153-168, 2001   DOI   ScienceOn
16 A. Lusson Cervantes, O. Agamennoni, and J. Figueroa, 'A nonlinear model predictive control system based on Weiner piecewise linear models,' Journal of Process Control, vol. 13, no. 5,pp.655-666,2003   DOI   ScienceOn
17 L. Giovanini and M Grimble, 'Robust predictive feedback control for constrained systems,' International Journal of Control and Systems, vol. 2, no. 4, pp. 407-422, 2004   과학기술학회마을
18 M. Morari and J. Lee, 'Model predictive control: Past, present and future,' Computers and Chemical Engineering, vol. 23, no. 9, pp. 667-682, 1999   DOI   ScienceOn
19 B. Aufderheide and B. Bequette, 'Extension of dynamic matrix control to multiple models,' Computers and Chemical Engineering, vol. 27, no. 11, pp. 1079-1096, 2003   DOI   ScienceOn
20 J. Lee and Z. Yu, 'Worst-case formulation of model predictive control for system with bounded parameters,' Automatica, vol. 33, no. 5, pp. 763-781, 1997   DOI   ScienceOn
21 L. Giovanini, 'Predictive feedback control,' ISA Transaction Journal, vol. 42, no. 2, pp. 207-226, 2003   DOI   ScienceOn
22 K. Dabke, 'A simple criterion for stability of linear discrete systems,' International Journal of Control, vol. 37, pp. 657-659, 1983   DOI   ScienceOn
23 M. Johanson and A. Rantzer, 'Computation of piecewise quadratic Lyapunov function for hybrid system,' IEEE Trans. on Automatic Control, vol. 43, no. 4, pp. 555-559, 1998   DOI   ScienceOn
24 D. Wang and J. Romagnoli, 'Robust model predictive control design using a generalized objective functions,' Computers and Chemical Engineering, vol. 27, no. 10, pp. 965-982, 2003   DOI   ScienceOn
25 N. de Oliveira and L. Biegler, 'An extension of Newton type algorithms for nonlinear process control,' Automatica, vol. 31, no. 2, pp. 281-286, 1995   DOI   ScienceOn
26 J. Adamy and A. Flemming, 'Soft variablestructure controls: A survey,' Automatica, vol. 40, no. 11, pp. 1821-1844, 2004   DOI   ScienceOn
27 G. Angelis, System Analysis, Modelling and Control with Poly topic Linear Models, Ph.D. Thesis, University of Eindhoven, 2001
28 V. Utkin, Sliding Modes in Control and Optimization, Springer-Verlag, 1992
29 K. Narendra and J Balakrishnan, 'Improvement transient response of adaptive control systems using multiple models and switching,' IEEE Trans. on Automatic Control, vol. 39, no. 9, pp. 1861-1866, 1994   DOI   ScienceOn
30 H. Su and T. Me Avoy, 'Artificial neural networks for nonlinear process identification and control,' in M. Henson and D Seborg (Eds.), Nonlinear Process Control (chapter 7), pp. 371428, Prentice-Hall, Englewood Cliffs, 1997
31 J. Roubos, S. Mollov, R. Babuska, and H. Verbruggen, 'Fuzzy model-based predictive control using Takagi-Sugeno models,' International Journal of Approximate Reasoning, vol. 22, no. 1, pp. 3-30, 1999   DOI   ScienceOn
32 T. Badgwell, 'Robust model predictive control of stable linear systems,' International Journal of Control, vol. 68, pp. 797-818, 1997   DOI