基于可拓博弈的智能汽车轨迹跟踪协调控制方法研究

Research on Intelligent Vehicle Trajectory Tracking Coordination Control Method Based on Extension Game

针对智能汽车在高速过弯工况下,轨迹跟踪误差大、横向稳定性无法保障的问题,提出一种可拓博弈轨迹跟踪协调控制方法,通过可拓划区域切换控制和博弈协调相结合,突破单一控制策略的工况适应性和多策略切换的抖动问题。所提方法基于分层控制体系,将轨迹跟踪控制分解为上层测度模式识别层和下层博弈协调层。上层基于可拓理论,提出并联可拓测度模式识别策略,将车-路系统实时状态映射至对应的可拓控制架构中经典域、可拓域和非域三种测度模式。下层针对不同测度模式对应设计三种控制策略,根据上层测度模式识别结果进行实时策略切换,引入博弈协调方法对并联可拓权重进行协调控制,有效避免了模式切换带来的抖动问题。通过Simulink/Carsim建立联合仿真模型,在双移线和“8字”形时变曲率高速工况开展算法对比验证,所提方法相较于比例-积分-微分(Proportion-integral-derivative,PID)控制方法,平均跟踪误差精度提升45.08%,尤其在大曲率突变的恶劣工况下,车辆稳定性提升44%。最后利用智能汽车试验平台进行了对比验证,对设计智能汽车高速轨迹跟踪控制策略具有极强的指导意义和参考价值。

For the problems of intelligent vehicle under high-speed cornering conditions,large trajectory tracking errors and lateral stability cannot be guaranteed,an extension game trajectory tracking coordination control method is proposed,which combines extension zone switching control and game coordination.It breaks the working condition adaptability of a single control strategy and the jitter problem of multiple strategy switching control.The proposed method is based on a hierarchical control system,which decomposes the trajectory tracking control into an upper measurement pattern recognition layer and a lower game coordination layer.Based on the extension theory,the upper layer proposes a parallel extension measurement pattern recognition strategy,and maps the real-time state of the vehicle-road system to the corresponding three measurement modes:classic domain,extension domain,and non-domain in extension control architecture.The lower layer designs three control strategies corresponding to different measurement modes.The real-time policy switching is performed based on the recognition results of upper measurement modes.The game coordination method is introduced to coordinate the parallel extension weights,which effectively avoids the jitter problem caused by mode switching control.The joint simulation model is established by Simulink/Carsim,and the algorithm is compared and verified in the double-shift and “8-shaped” with time-varying curvature and high-speed conditions.Compared with the ProportionIntegral-Derivative(PID) control method,the proposed method improves the average tracking error accuracy by 45.08 %,especially under bad working conditions with sudden changes in curvature,vehicle stability is improved by 44%.Finally,the intelligent vehicle experiment platform is used for comparison and verification,which has strong guiding significance and reference value for designing high-speed trajectory tracking control strategies for intelligent vehicles.