Minimum Variance Controls-Configured Systems.
Abstract
The problem of optimal regulation for stochastic linear systems is examined and posed as a mathematical programming problem. The approach taken is that of simultaneously configuring both the plant and controller, thereby producing what is called a controls-configured system. A computational procedure, built around the Generalized Reduced Gradient algorithm, is developed using the state convariance as the measure of system regulation, hence yielding a minimum variance, controls-configured system design. Two examples are presented that illustrate the procedure in which regulation is improved by approximately 18% over that achievable with a fixed plant. The stabilization of a linear time-invariant system is examined using state variable feedback. A mathematical programming algorithm is presented which permits the selection of a feedback gain matrix that yields, subject to a prescribed constraint set, a stable closed-loop solution. The technique is applied to a sample problem and appears well-suited for the stabilization problem.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1975
- Accession Number
- ADA013859
Entities
People
- Roger F. Roberts
Organizations
- University of California, Irvine