Multiobjective controller design handling human preferences

Trends in controller design point to the integration of several objectives to achieve new performances. Moreover, it is easy to set the controller design problem as an optimization problem. Therefore, future improvements are likely to be based on the adequate formulation and resolution of the multiobjective optimization problem. The multiobjective optimization strategy called physical programming provides controller designers with a flexible tool to express design preferences with a ‘physical’ meaning. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories such as desirable, tolerable, unacceptable, etc. to which numerical values are assigned. The problem is normalized and converted to a single-objective optimization problem but normally it results in a multimodal problem very difficult to solve. Genetic algorithms provide an adequate solution to this type of problems and open new possibilities in controller design and tuning.