Accession Number : AD0746056

Title :   Adaptive Control of Stochastic Linear Systems with Unknown Parameters.

Descriptive Note : Master's thesis,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ELECTRONIC SYSTEMS LAB

Personal Author(s) : Ku,Richard Tse-Min

Report Date : MAY 1972

Pagination or Media Count : 156

Abstract : The thesis considers the problem of optimal control of linear discrete-time stochastic dynamical system with unknown and, possibly, stochastically varying parameters on the basis of noisy measurements. It is desired to minimize the expected value of a quadratic cost functional. Since the simultaneous estimation of the state and plant parameters is a nonlinear filtering problem, the extended Kalman filter algorithm is used. The open-loop feedback optimal control technique is investigated as a computationally feasible solution to the adaptive stochastic control problem. The open-loop feedback optimal control system adaptive gains depend on the current and future uncertainty of the parameters estimation. Thus, the standard Separation Theorem does not hold in this problem. Suboptimal control system in which Separation Theorem is arbitrarily enforced is also considered. (Author)

Descriptors :   (*ADAPTIVE CONTROL SYSTEMS, MATHEMATICAL MODELS), LINEAR SYSTEMS, STOCHASTIC PROCESSES, MATRICES(MATHEMATICS), FEEDBACK, WHITE NOISE, OPTIMIZATION, COMPUTER PROGRAMS, THESES

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE