Control system design applications with hybrid genetic algorithms

TL;DRAbstract

This thesis investigates the hybrid application of stochastic and heuristic algorithms, in particular genetic algorithms (GA), simulated annealing (SA) and Greedy search algorithms for the design of linear and nonlinear control systems. We compare the rate of convergence, computational effort required (FLOPS) and ease of implementation. Where possible, results are compared with the more traditional control system design methodologies. Two specific practical applications include aircraft flight control systems, and a nonlinear example of an industrial bioreactor fermentation process. Stochastic algorithms (GA) and heuristic algorithms (SA, Greedy, Tabu search) are powerful search methods, capable of locating the global minimum or maximum (extremum) of multimodal functions. They operate without the need for function gradients and are robust to noisy data. The current research trend is directed towards the solution to constrained multiobjective optimization problems of multimodal function

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