智能小车多变量协同模糊PID控制算法
Multivariable Cooperative Fuzzy PID Control Algorithm for Intelligent Car

智能小车是实验室研究无人驾驶技术的一个载体。本文针对传统的舵机和电机PID分散控制方法无法体现智能小车横向偏转角和纵向速度之间的协同关系及熟练驾驶员的推理经验的问题，设计了一种改进的智能小车纵向速度、横向偏摆角的多变量协同控制算法。仿照人类的推理方式，构建四维模糊调整“规则块”阵列，并针对小车纵向速度、偏摆角的特点对输出参数进行归一化与线性加权调整，以实现最终的多变量协同控制。并通过实验室环境下小车的实车实验，验证了该方法的可行性和有效性。
Intelligent car is a carrier of laboratory research on unmanned technology. Aiming at the problem that the traditional PID decentralized control method of actuator and motor cannot reflect the experience of skilled drivers and the collaborative relationship between the lateral deflection angle and the longitudinal speed, this paper builds a four-dimensional fuzzy adjustment “rule block” array by imitating the way of human reasoning. The output parameters are normalized and linearly weighted to achieve the final multivariable cooperative control according to the characteristics of the car’s longitudinal speed and yaw angle. Finally, it is verified that the new method proposed in this paper is feasible and effective through the real vehicle experiment of laboratory car.