Genetic algorithms for optimal feedback control design

✍ Scribed by Sourav Kundu; Seiichi Kawata

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 777 KB
• Volume: 9
• Category: Article
• ISSN: 0952-1976
• DOI: 10.1016/0952-1976(96)00034-6

✦ Synopsis

This paper presents a technique for optimal feedback control design which combines a relatively recent artificial intelligence (AI) method, the genetic algorithm ( GA ), and the more traditional methods of control system design, achieved via a new problem formulation. The performance function of a control system is generally formulated as a linear combination of xT Qx and ur Ru, where Q is the state weighting matrix and R is the control weighting matrix. These matrices are difficult to ascertain in real-worM cases. The approach outlined here formulates the optimal feedback control design as a multiple-criteria problem, thereby avoiding use of the weighting matrices. It is shown that using the proposed problem formulation, a non-linear state feedback can also be implemented, which expands the search space for the design. A numerical example is computed to show the efficacy of such a method.