Title :
A self-tuning minimum variance controller based on multivariable multisample models
Author :
Aude, Elina Prado Lopes ; Sandoz, David J.
Author_Institution :
Fed. Univ. of Rio de Janeiro, Brazil
Abstract :
A minimum-variance self-tuning control algorithm applicable to multivariable systems represented by multisample input-output models is proposed. The theoretical development of the algorithm is analyzed in detail, and its performance is compared with that of an linear quadratic Gaussian (LQG) algorithm through practical control experiments using a micromachine system. As the minimum variance algorithm is implicit, it demands less computational load to evaluate the control action than the LQG algorithm. Consequently, the minimum variance algorithm seems to be more suitable for use with faster systems
Keywords :
adaptive control; controllers; machine control; multivariable control systems; self-adjusting systems; small electric machines; computational load; control action; linear quadratic Gaussian; micromachine system; multivariable multisample models; self-tuning minimum variance controller; Character generation; Control systems; Cost function; Delay; Equations; Optimal control; Polynomials; Steady-state; White noise;
Conference_Titel :
Circuits and Systems, 1989., Proceedings of the 32nd Midwest Symposium on
Conference_Location :
Champaign, IL
DOI :
10.1109/MWSCAS.1989.102002
