Parameter Optimization of Steering Trapezoid Mechanism Based on Hybrid Genetic Algorithm

Optimization of the steering trapezoid mechanism parameter has great significance for improving vehicular handling performance and steering safety. The mathematical model of the current trapezoid mechanism design is oversimplified; Thus, the value of the optimum parameter is often not achievable. In this paper, a design model for the trapezoidal steering mechanism is proposed taking into consideration the size and kinematic constraints. Based on combining Ackerman's principle and spatial geometric relation, a multi-body dynamics design method is used to derive a nonlinear optimization model of the split steering trapezoid mechanism. In this investigation, a hybrid genetic algorithm is developed to minimize the steering error and the corresponding optimum design parameters. The selected design parameters are the bottom angle and the steering arm length of steering trapezoid mechanisms. The objective function of the structural optimization is a weighted summation of the relative error between the theoretical rotational and actual rotational angles of the front axle. The derived mathematical model of the mechanism is verified through the steering experiment of the tested vehicle. According to the initial design, an independent front suspension model is established in ADAMS, and an Ackerman error simulation experiment is performed. The results of the proposed algorithm and ADAMS simulation show that the maximum angle error of the steering mechanism is 0.91° at the maximum rotational angle range. The optimized steering trapezoid mechanism improves the performance of steering angles in two directions, tracking the ideal Ackerman angle and also reduce tire wear while maintaining the automobile's tuning stability.