Research on Optimization of Initial Parameters of Semi-active Seat Suspension Based on Genetic Algorithm
Semi-active seat suspension can effectively improve driving smoothness, reduce human vertical frequency, and improve the riding experience of drivers and occupants in the car. Among them, the initial performance parameters of the semi-active suspension play a decisive role in the suspension performance. Therefore, this paper studies the optimization of the initial performance parameters (spring stiffness and damping coefficient) of semi-active seat suspension, and optimizes the stiffness and damping coefficient of semi-active seat suspension by using genetic algorithms on the basis of establishing a random road model and a six-degree-of-freedom half-vehicle model, hence optimizing the ratio of vertical acceleration of the front and rear seats as the fitness function. Finally, a simulation analysis of the optimization results is carried out by using MATLAB software, leading to results showing that after algorithm optimization, the root mean square of the vertical acceleration of the seat is reduced by 16.4％, and the maximum value is reduced by 11.3％; the root mean square of the relative displacement of the seat is reduced by 36％, and the maximum value reports a 39.2％ decrease.
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