基于蚁群算法寻优的电动轮汽车牵引力PID控制

Traction PID Control Based on Ant Colony Optimization for Electric Wheeled Vehicle

  • 摘要: 提出了一种基于蚁群算法优化PID参数的控制策略,并应用于电动轮汽车的牵引力控制.文中提出了一种易于工程应用的方法来实时估计车辆的状态参数,设计模糊控制器计算出最佳滑转率,将ACO应用到牵引力PID控制器中从而实现对车轮转矩的调节,并在搭建的电动轮汽车中进行实车测试.结果表明,所制定的控制策略可以满足要求,抑制了车轮的过度滑转.

     

    Abstract: A traction control strategy based on PID and ant colony algorithm (ACO) was proposed to improve the longitudinal acceleration stability of electric wheeled vehicles. First, a method of engineering application was described to estimate the state parameters of vehicles in real time. Then the optimal slip rate was calculated with a designed fuzzy controller. Finally, the ACO was applied to the traction PID controller, to achieve the adjustment of the wheel torque and carry out the real vehicle test in the electric wheel car. The results show that the control strategy can meet the requirements and restrain the excessive slip of the wheels.

     

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