Optimal Regulation of Stochastic Linear Systems with Adjustable Parameters,
CALIFORNIA UNIV IRVINE SYSTEMS ENGINEERING AND OPERATIONS RESEARCH GROUP
Pagination or Media Count:
The problem of optimal regulation for stochastic linear systems with adjustable plant parameters is examined and posed as a nonlinear programming problem. A computational procedure, built around the Generalized Reduced Gradient algorithm, is developed to solve the associated plant-controller design problem. The procedure is illustrated via a lateral autopilot design in which the quality of regulation is improved by approximately 18 over that achievable with a nominal fixed plant.
- Statistics and Probability