Direct learning neural controller tuned by a real number genetic algorithm
This paper establishes the utility of a real number genetic algorithm (RGA) for direct learning neural control. The controller has a hierarchical structure where the learning algorithm, RGA, is the higher level and a feedback neural controller is the lower. The salient characteristics of the controller are 1) learning capabilities to improve plant tracking performance from one repetition to the next, 2) little or no prior knowledge of the plant dynamics is required, and 3) no need for online plant identification. The proposed controller is validated on two nonlinear plants in simulation. Results also include effects of changing the structure of the neural controller on trajectory tracking.
neural control genetic algorithms learning control gradient-free optimization