Title :
Adaptive control with stochastic gradient algorithm: rate of convergence
Author :
Radenkovic, Miloje S.
Author_Institution :
Dept. of Electr. Eng., Colorado Univ., Denver, CO, USA
Abstract :
In this paper, we consider the rate of convergence of the parameter estimation error and the cost function for the stochastic approximation type algorithm. The problem is solve in the case of the minimum-variance stochastic adaptive control. It is proven that stochastic approximation algorithm has the same rate of convergence as the one established for the least-squares algorithm. Comparison of the two algorithms is made under the same conditions related to the process noise and the structure of the system model
Keywords :
adaptive control; convergence of numerical methods; discrete time systems; least squares approximations; parameter estimation; stability; stochastic processes; SISO systems; adaptive control; convergence; cost function; discrete time systems; least-squares algorithm; minimum-variance; parameter estimation error; process noise; stability; stochastic approximation; stochastic gradient algorithm; system model; Adaptive control; Adaptive systems; Approximation algorithms; Convergence; Cost function; Parameter estimation; Polynomials; Stochastic processes; Stochastic resonance; Stochastic systems;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.529774
