Abstract
An approach to the approximate solution of general control problems, exploiting evolutionary global search and optimization techniques, is proposed. According to the methodology developed in this paper, controls are approximated with the help of appropriately chosen parameter vectors and substituted in system equations. Alternatevly, the phase coordinates may be parameterised, especially, when it is possible to represent controls as single valued functions of the states. In both cases the components of the parameter vectors are regarded as variables of a performance index based goal function that is to be minimized with respect to the system constraints. Such an approach enables modeling and solution of a wide class of optimal control problems, arising in engineering practice, within a unified framework of constrained optimization techniques, including evolutionary algorithms for global optimization and multiobjective control, and utilization of parallel processing to alleviate the computational burden in high dimensional optimal control problems.
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