Genetic algorithms for multiobjective controller design

Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a 'physical' sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a unique objective optimization but normally as a multimodal problem. This work shows how to convert a robust control design problem into a multiobjective optimization problem and to solve it by Physical Programming and Genetic Algorithms. An application to the American Control Conference (ACC) Robust Control Benchmark is presented and compared with other known solutions.