基于流挖掘的分布式驱动电动汽车横向稳定控制

Lateral Stability Control of Distributed Drive Electric Vehicle Based on Data Stream Mining

以分布式驱动电动汽车为研究对象,为减轻驾驶员行为与汽车执行机构响应之间的时间延迟对车辆稳定性控制的不利影响,提出一种基于转向状态预测的稳定性分层控制策略。基于马尔可夫模型构建状态转移概率矩阵,并利用流挖掘技术实时更新状态转移概率矩阵,联合实现下一时刻转向状态的预测。仿真结果表明,与滑模控制相比,所提出的策略可以更准确地跟踪理想横摆角速度,避免横向失稳情况的发生。

To mitigate the adverse effect of time delay between the driver’s behavior and the response of the vehicle actuator on vehicle stability control, we take the distributed drive electric vehicle as the research object, and propose a stability hierarchical control strategy based on steering state prediction. The state transition probability matrix is constructed based on Markov model, which is updated by the data stream mining technology in real time, and the two jointly achieve the goal of predicting the steering state at the next moment. The simulation results show that, compared with sliding mode control, the proposed strategy can track the ideal yaw rate more accurately, and avoid the occurrence of lateral instability.