Title :
Stochastic adaptive control using multiple estimation models
Author :
Narendra, Kumpati S. ; Driollet, Osvaldo A.
Author_Institution :
Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA
Abstract :
The use of multiple models for adaptively controlling an unknown continuous-time linear system has been proposed (K.S. Narendra and J. Balakrishnan, 1994, 1997). K.S. Narendra and C. Xiang (2000) extended the same concepts to discrete-time systems, both for the noise-free case as well as when a stochastic disturbance is present, and the convergence of the algorithms was established. In this paper, we consider structurally different estimation models and use the multiple models approach to select, online, the one that results in the best performance of the overall system for the given disturbance characteristics. The objective is to demonstrate that the convergence of these schemes can be treated in a unified manner. Simulations are included to indicate the improvement in performance that can be achieved using such schemes
Keywords :
adaptive control; convergence; discrete time systems; parameter estimation; performance index; stochastic systems; convergence; discrete-time systems; disturbance characteristics; multiple estimation models; online model selection; performance; simulations; stochastic adaptive control; Adaptive control; Control systems; Convergence; Equations; Linear systems; Polynomials; Production facilities; Stochastic processes; Stochastic resonance; Stochastic systems;
Conference_Titel :
American Control Conference, 2001. Proceedings of the 2001
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-6495-3
DOI :
10.1109/ACC.2001.945945
