Accession Number : ADA288359

Title :   Robust Hybrid State-Space Self-Tuning Control Using Dual-Rate Sampling.

Descriptive Note : Final rept. 1 May 91-31 Aug 94,

Corporate Author : HOUSTON UNIV TX DEPT OF ELECTRICAL ENGINEERING

Personal Author(s) : Shieh, L. S. ; Koc, C. K.

PDF Url : ADA288359

Report Date : 31 AUG 1994

Pagination or Media Count : 10

Abstract : This report presents a hybrid state-space self-tuner using a new dual-rate sampling scheme for digital adaptive control of continuous-time uncertain linear systems. A state-space-based recursive least-squares algorithm, together with a variable forgetting factor, is used for direct estimation of both the equivalent discrete-time uncertain linear system parameters and associated discrete-time state of a continuous-time uncertain linear system from the sampled input and output data. An analog optimal regional pole-placement design method is used for designing an optimal observer-based analoque controller. A sub-optimal observer-based digital controller is then designed from the designed analoque controller using digital redesign technique. To enhance the robustness of parameter identification and state estimation algorithms, a dynamic bound for a class of uncertain bilinear parameters and a fast-rate digital controller are developed at each fast-sampling period. Also, to accommodate computation loads and computation delay for developing the advance hybrid self-tuner, the designed analoque controller and observer gains are both updated at each slow-sampling period. This control technique has been successfully applied to benchmark control problems.

Descriptors :   *LINEAR SYSTEMS, *DIGITAL SYSTEMS, *ADAPTIVE CONTROL SYSTEMS, ALGORITHMS, INPUT, OUTPUT, COMPUTATIONS, DYNAMICS, PARAMETERS, ESTIMATES, TIME, IDENTIFICATION, SAMPLING, DELAY, DISCRETE DISTRIBUTION, STANDARDS, TUNING, SELF OPERATION, ABSTRACTS.

Subject Categories : Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE