基于预测--模糊联合控制的分布式驱动车辆路径跟踪方法

为研究分布式驱动车辆（4wid）路径跟踪中的跟踪精度和稳定性的问题,提出了模型预测-模糊(Mpc-Fuzzy)的联合控策略, 缓解了单一模型预测控制下车辆跟踪精度与控制器计算压力间的冲突。采用模型预测控制设计了上层路径跟踪控制器,旨在利用模型预测“滚动优化”的优点降低跟踪误差的同时保证车辆的稳定性。下层采用模糊控制,将转向过度和转向不足量化为“方向偏差”和“位置偏差”,利用四轮转矩独立控制的优点进一步优化转矩输出,旨在降低模型预测控制器对参考模型线性化后带来的跟踪误差。为验证控制策略的可靠性,基于Carsim和Simulink.、Driving Scenario搭建联合仿真模型,将联合控制器和单运动学模型预测控制器进行对比研究。仿真结果表明联合控制器可在完成对给定路径跟踪的基础上,减少转向不足和转向过度的发生,降低了方向误差和位置误差。

A path tracking Method For 4WID vehicle Based On Mpc-Fuzzy Controller

To improve the path tracking accuracy and stability of four-wheel-independent-drive (4WID) electric vehicle, a combining control strategy of model predictive and fuzzy is proposed, which aiming at alleviating the conflict between vehicle tracking accuracy and controller calculation load. At the same time, to reduce the tracking error, model predictive control was used to design the upper path tracking controller. The fuzzy control was used in the sub-layer to quantify the over-steer and under-steer as "direction deviation" and "position deviation". The output torque is further optimized by the fuzzy controller, which aiming at reducing the tracking error caused by the linearization of the model predictive controller. To verify the reliability of the control strategy, a comparative study was conducted on the combining control strategy and the single kinematic model predictive control strategy. The results of simulation show that the occurrence of under-steer and over-steer are reduced, and simultaneously, the direction error and position error are also reduced when tracking the given path.