An algorithmic parameter tuning methodology for controller design of complex systems is needed. This methodology should offer designers a great degree of flexibility and give insight into the potentials of the controller structure and the consequences of the design decisions that are made. Such a method is proposed here. For an exploratory phase a new pareto-ranked genetic algorithm is proposed to generate an evenly dispersed set of near optimal, global, solutions. By pair-wise preference statements on design alternatives a linear program is set up as a formal means for selecting the solution with best overall designer satisfaction. In a following interactive design phase using nonlinear programming techniques with a priori decisions on allowed quality levels, a best tuning compromise in competing requirements satisfaction is searched for while guaranteeing pareto-optimality. In particular, this two-phase tuning approach allows the designer to balance nominal control performance and multi-model control robustness.
CITATION STYLE
Kolossa, D., & Grübel, G. (2000). Evolutionary computation and nonlinear programming in multi-model-robust control design. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1803, pp. 147–157). Springer Verlag. https://doi.org/10.1007/3-540-45561-2_15
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