Title :
Robustness of extended least squares based adaptive control
Author :
Naik, Sanjeev M. ; Kumar, P.R.
Author_Institution :
Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA
Abstract :
The authors show that the popular extended least-squares-based one-step ahead adaptive tracking algorithm is robust to both the presence of small unmodeled dynamics and violation of the stochasticity assumption on the noise. Specifically, the noise is allowed to be any bounded sequence. The only modification used is a projection of the parameter estimates. The adaptive control algorithm is a weighted extended least-squares type noninterlaced adaptation law with projection. Assuming the nominal plant to be minimum-phase, without the small unmodeled dynamics, it is proved that all the signals in the closed-loop system are uniformly bounded
Keywords :
adaptive control; closed loop systems; dynamics; parameter estimation; position control; stability; adaptive tracking algorithm; closed-loop system; extended least squares based adaptive control; minimum phase plants; parameter estimates; robustness; stability; unmodeled dynamics; Adaptive control; Control systems; Least squares approximation; Least squares methods; Noise robustness; Parameter estimation; Polynomials; Robust control; Stochastic systems; White noise;
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
DOI :
10.1109/CDC.1991.261412
