A new adaptive least-squares estimation algorithm with generalized internal feedback

• Autorzy: Latawiec K. J.
• Języki publikacji: EN

Abstrakty

EN

New concepts of 'covariance matrix normalization' and the 'cascade structure' of the adaptive least-squares parameter estimator are shown to generalize and extend the use of internal information feeback in various robustness/alertness-oriented modifications to the standard ALS estimation algorithm. In the cascade estimation structure it is possible to 'naturally' stabilize, rather than maximize, the information matrix so that covariance zeroing and blowup are effectively eliminated and the celebrated square root update of the covariance matrix is no longer needed. Consequently, a new, partly heuristic ALS MIMO estimation algorithm, enabling to effectively track both slow and jump parameter variations, is presented. The algorithm is coupled with a simple but robust predictive control scheme, offering a new adaptive MIMO control strategy.

Słowa kluczowe

EN

adaptive systems; least squares methods; robust estimation; time-varying systems; adaptive control; predictive control

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2004
• Tom: Vol. 14, no. 1
• Strony: 45--58
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Latawiec K. J.

• Department of Electrical Engineering and Automatic Control Technical University of Opole, Poland,

Bibliografia

• [1] L.V.R. Arruda and G. Favier: A review and comparison of robust estimation methods. Proc. 10th IFAC Symp. on Identification and System Parameter Estimation, Budapest, Hungary, (1991), 1027-1031.
• [2] K.-J. Astrom and B. Wittenmark: Adaptive control. Addison-Wesley, NY, 1989.
• [3] S. Bittanti and M. Campi: Bounded error identification of time-varying parameters by RLS techniques. IEEE Trans. on Automatic Control, 39 (1994), 1106-1110.
• [4] A. Datta: Robustness of discrete-time adaptive controllers: an input-output approach. IEEE Trans. on Automatic Control, 38 (1993), 1852-1855.
• [5] C. Diaz, P. Dieu, Ph. Lelong, C. Feuillerat and M. Salome: Robust adaptive predictive control of the dissolved gaseous environment of submerged microbial processes. Int. J. Adaptive Control and Signal Processing, 12 (1998), 365-380.
• [6] C. Fargeon (Ed.): The Digital Control Systems, North Oxford Academic, 1989, London; (Chapter 6: Adaptive Control of Stochastic Systems).
• [7] G. Feng And M. Palaniswami: Robust direct adaptive controllers with a new normalization technique. IEEE Trans. on Automatic Control, 39 (1994), 2330-2334.
• [8] F. Giri, J.M. Dion, L. Dugard and M. M’saad: Parameter estimation aspects in adaptive control. Automatica, 27 (1991), 399-402.
• [9] R. Kulhavy: Restricted exponential forgetting in real-time identification. Automatica, 23 (1987), 589-600.
• [10] I. D. Landau, M. Samaan and M. M’saad: Robust performance oriented adaptive control for bio-technological processes - a tutorial. Proc. American Control Conf., San Diego, USA, (1990), 2681-2687.
• [11] K. J. Latawiec: The cascade structure in adaptive least-squares parameter estimation. Proc. 3rd Int. Symp. on Methods and Models in Automation and Robotics, Międzyzdroje, Poland, (1996), 643-648.
• [12] K. J. Latawiec: Extended horizon adaptive model algorithmic control. Proc. 11th IFAC Symp. on System Identification, Kitakyushu, Japan, (1997), 297-302.
• [13] K. J. Latawiec: Contributions to advanced control and estimation for linear discrete-time MIMO systems. Technical University of Opole Press, Opole, Poland, 1998.
• [14] K. J. Latawiec and R. Rojek: Ehmac - a new simple tool for robust linear multivariable control. Int. J. of Applied Mathematics and Computer Science, 10(3), (2000), 101-116.
• [15] L. Ljung: System identification. Prentice-Hall, Englewood Cliffs, NJ, 1987.
• [16] L. Ljung and S. Gunnarsson: Adaptation and tracking in system identification, a survey. Automatica, 26 (1990), 7-21.
• [17] A. Niederliński: An upper bound for the recursive least squares estimation error. IEEE Trans. on Automatic Control, 40 (1995), 1655-1661.
• [18] A. Niederliński: Error convergence rate for multi-recursion RLS estimation in linear multi-step prediction models. Archives of Control Sciences, 4(3/4), (1995), 173-202.
• [19] R. Ortega, L. Praly and I. D. Landau: Robustness of discrete-time adaptive controllers. IEEE Trans. on Automatic Control, 30 (1985), 1179-1187.
• [20] R. Ortega and Y. Tang: Robustness of adaptive controllers - a survey. Automatica, 25 (1989), 651-677.
• [21] T. Soderström and P. Stoica: System identification. Prentice-Hall, Englewood Cliffs, NJ, 1989.
• [22] T.-W. Yoon and D. W. Clarke: Adaptive predictive control of the benchmark plant. Automatica, 30 (1994), 621-624.