The existing research of the active suspension system（ASS） mainly focuses on the different evaluation indexes and control strategies. Among the different components, the nonlinear characteristics of practical systems and control are usually not considered for vehicle lateral dynamics. But the vehicle model has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, the nonlinear dynamic model of lateral system is considered and also the adaptive neural network of tire is introduced. By nonlinear analysis methods, such as the bifurcation diagram and Lyapunov exponent, it has shown that the lateral dynamics exhibits complicated motions with the forward speed. Then, a fuzzy control method is applied to the lateral system aiming to convert chaos into periodic motion using the linear-state feedback of an available lateral force with changing tire load. Finally, the rapid control prototyping is built to conduct the real vehicle test. By comparison of time response diagram, phase portraits and Lyapunov exponents at different work conditions, the results on step input and S-shaped road indicate that the slip angle and yaw velocity of lateral dynamics enter into stable domain and the results of test are consistent to the simulation and verified the correctness of simulation. And the Lyapunov exponents of the closed-loop system are becoming from positive to negative. This research proposes a fuzzy control method which has sufficient suppress chaotic motions as an effective active suspension system.
为提高混合动力电动汽车(Hybrid electric vehicle, HEV)整车性能,结合等效燃油消耗最小模型,提出一种多智能体(Agent)控制的动力总成集成控制策略。系统Agent实时分解整车动力总成任务并将其分配给发动机Agent、电机Agent和蓄电池Agent,各部件Agent计算完成任务所需付出的油耗(或等效油耗)、排放与蓄电池能量损耗成本,采用多目标拟合优化算法求取综合成本最小的动力分配关系,得到初步请求响应转矩指令。各部件Agent以自身工作效率优化为目标对请求响应转矩进行限制,并与其他Agent交互补偿转矩信息,协调协作完成HEV动力总成的集成控制。在Simulink中建立各Agent模型和集成控制策略模型,嵌入到ADVISOR整车模型中进行不同仿真循环工况下的联合仿真。研究结果表明,集成控制策略能够能实时合理权衡动力性、排放性和燃油经济性,在未损失较多动力性的前提下能够提高HEV节能减排能力。
