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A robust neural NARX controller design using evolution and learning is proposed in this paper. Parameters of neural NARX controller are encoded in individual member (chromosome) of GA population. Neural NARX controller learns stabilizing response at an operating point of plant as best fitness reaches large steady value during successive generations. Stable operating region of neural NARX controller is enlarged by incrementally allowing learn more operating points. Best neural NARX controller in last generation is saved as designed neural NARX controller. Proposed approach is validated on a cart pole nonlinear plant model. Optimal stabilizing response with designed neural NARX controller over wide range of operating points is obtained. KeywordsNon-linear auto regressive with exogenous input (NARX), Reinforcement (RF) signal, Performance index (PI) Genetic algorithms (GAs).

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