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Optimal composite nonlinear feedback with multi-objective genetic algorithm for active front steering system

In designing an optimal composite nonlinear feedback (CNF) controller, the parameter estimation of linear feedback gain and nonlinear gain parameters are important to produce the best output response. An optimization algorithm is designed to minimize the time consuming to get the best parameter. To design an optimal method, Multi Objective Genetic Algorithm (MOGA) is utilized to optimize the CNF controller performance. Those parameters will be optimized based on the time response specifications namely overshoot, settling time and steady state error in solving the minimization problem. By the implementation of multi-objective approach, all of these criteria can be computed together in one optimization algorithm by using linear weight summation. The application of this investigation is implemented on active front steering system (AFS) system. The important vehicle parameter that must be controlled in AFS is yaw rate response. However, the response of the yaw rate needs to follow the desired yaw rate reference to achieve a good handling performance. Thus, by the implementation of CNF with MOGA in AFS system, the yaw rate response is improved and able to track the desired reference response.