Optimal Control Design Using Evolutionary Algorithms with Application to an Aircraft Landing System

This research presents novel approaches for selecting appropriate weighting matrices for desired Linear Quadratic Regulator (LQR) controller design using evolutionary algorithms. The genetic algorithm (GA) is the most popular method of the supposed evolutionary methods in the controller design. The tabu search (TS) strategy is a local search method with a flexible memory structure and it can be applied directly on many problems, without requiring the plant models. The particle swarm optimization (PSO) method is a stochastic method which is shown to have a valuable improvement to the controller design procedure. Obviously, it is not easy to determine the appropriate weighting matrices for an optimal control system and a suitable systematic method is not presented for this goal. Therefore, there is not straight relationship between weighting matrices and control characteristics and selecting these matrices is done using trial and error based on designer’s experience. Stable convergence characteristics and high calculation speed are advantages of the proposed method. An Aircraft Landing System is considered as a case study. Compared with GA and TS, PSO method is more efficient and robust with better control performance. Also, energy of the control signals for GA, TS and PSO approaches are calculated and compared with each other which confirms the superiority of PSO method again. Thus, the PSO method is very efficient and robust in designing of optimal controller.

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