Control system design unification and automation - A way forward in CACSD via evolutionary computation

Abstract

This paper proposes a performance-prioritized computer aided control system design (CACSD) methodology using a high-performance multi-objective evolutionary algorithm toolbox. Unlike conventional mutually independent control schemes, the evolutionary CACSD approach unifies different control laws in both the time and frequency domains based upon performance satisfactions, without the need of aggregating different design criteria into a compromise function or formulating the problem in a specific domain for linear parameterization and deterministic convex optimization. It is shown that control engineers' expertise as well as settings on goal and priority for different preference on each performance requirement can be easily included and modified on-line according to the evolving trade-offs, which makes the controller design interactive, transparent and simple for real-time implementation. Advantages of the proposed evolutionary CACSD methodology are illustrated upon a practical ill-conditioned distillation system, which offers a set of low-order Pareto optimal controllers that satisfy all the required performance specifications in the face of system constraints.