Solving Multi-Objective Linear Control Design Problems Using Genetic Algorithms
نویسندگان
چکیده
Two multi-objective genetic algorithms, an elitist version of MOGA and NSGA-II, were applied to solve two linear control design problems. The first was a H2 problem with a PI controller structure, for a first order stable plant. The second was a mixed H2/H4 control problem. In both cases, three indicators were used to evaluate each algorithm performance: Set coverage, spread and hypervolume. It was found that NSGA-II shows better performance indicators. Moreover, for the second problem, a new controller representation was proposed with corresponding cross-over and mutation operators. This approach was able to find solutions as good as those previously published. The main advantage is that the stability restriction disappears, allowing to deal with an unconstrained optimization problem.
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