Design of fixed structure controllers with respect to various specifications using robust control and genetic algorithm

Mauff, Frédéric Le; Duc, Gilles

doi:10.1109/acc.2002.1025457

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2003

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“…Although genetic algorithms do not guarantee convergence to an optimum solution the high point of genetic algorithms is that they provide near global optima for many complex problems in practice. The algorithms have been used to solve a range of complex problems from combinatorial problems such as the travelling salesman problem [9] to many problems in optimal control [15][16][17][18][19][20][21][22]. The success of genetic algorithms for solving a particular problem depends on primarily on variable representation (real, binary, combinatorial etc.)…”

Section: Introductionmentioning

confidence: 99%

Reduced order optimal control using genetic algorithms

Chellaboina

Ranga²

Proceedings of the 2005, American Control Conference, 2005.

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In this paper, we present a case study of several examples involving optimal positive real control design for positive real plants. Specifically, we apply genetic algorithms to obtain low order optimal control designs with and without the strict positive real constraint on the compensator. This study shows that genetic search strategy is an effective approach for designing low order optimal controllers with complex constraints such as strict positive realness of the compensator. Furthermore, the examples studied also shed some new light on optimal positive real control problems.

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