面向山区高速公路复杂工况的重型半挂车适时模式切换的稳定性集成控制研究

The existing articulated heavy vehicle control system can’t achieve the good control the vehicle under the complex conditions which are steep slopes and big bend of mountainous highway. To solve the problem, the integrated control system, which the subsystems are fused and coordinated, is proposed. The research focuses on the road parameters estimation, the influence mechanism that road parameters are on vehicle stability and the timing mode switching mechanism considering dynamic variation of road parameters. The longitudinal gradient, transverse slope, curve radius and road adhesion coefficient are estimated based on nonlinear complex model. A neural network model of road parameters and vehicle stability is established; then the influence mechanism is revealed. The dynamic threshold and corresponding control mode, which are modified by the road parameters, are obtained; and the timing mode switching mechanism is presented. Considering the dynamic change of road parameters, the control weight coefficients are optimized by the genetic particle swarm optimization algorithm to solve the multiple single subsystems fusion coordination problem, and the optimal control is achieved. The weighted coefficient MAPs of the full working condition are acquired by the optimized weighted coefficients. The integrated control strategy is based on the linear variable real-time parameters simplified model and adopts the timing mode switching mechanism. The time precise optimal control for all working conditions is achieved by the integrated control strategy. Finally, the hardware in the loop bench is built and the proposed control strategy is tested. The research results will provide a good theoretical basis and technical means for articulated heavy control under mountainous highway.

针对现有重型半挂车控制系统无法实现山区高速公路坡陡弯大等复杂工况良好控制的难题，构建重型半挂车子系统融合协调的集成控制系统，重点研究路面参数估计，路面参数对车辆稳定性影响机理以及考虑路面参数动态变化的适时模式切换机制。建立非线性复杂模型，准确估计路面纵向坡度、横向坡度、弯道半径和路面附着系数值；建立路面参数与车辆稳定性的神经网络模型，揭示路面参数对车辆稳定性影响机理，获得经路面参数等修正后的动态门限及对应控制模式，进而提出适时模式切换机制。融合路面参数，运用遗传粒子群算法优化控制权重系数，解决多个单一子系统融合协调问题，实现集成控制策略最优控制，并形成全工况权重系数MAP。采用线性变参数实时简化模型，利用适时模式切换机制进行适时模式切换，实现集成控制策略全工况实时精准最优控制。搭建硬件在环实验台，并对控制策略进行实验验证。研究成果将为山区高速公路重型半挂车控制提供良好的理论基础和技术手段。

因山区高速公路坡陡弯大；同时，重型半挂车载重量重、尺寸大、质心高，且牵引车和挂车之间相互耦合，造成山区高速公路复杂工况的重型半挂车稳定性精准最优控制困难。针对此难点，本项目在建立简化模型基础上，辨识了简化模型关键参数并形成关键参数MAP，进而形成了线性变参数简化模型；利用考虑了路面参数的非线性车辆模型，采用双无迹卡尔曼滤波算法，实现了路面附着系数等路面参数和车辆状态准确估计，参数最大误差率在2.67%范围内，状态最大误差率在1.88%范围内；利用路面参数和车辆状态与车辆稳定性的关系数据，采用神经网络算法，获得了路面参数对车辆稳定性影响机理，进而形成了适时模式切换机制，奠定了复杂工况的稳定性集成控制策略精准控制基础。.基于线性变参数实时简化模型，提出了重型半挂车适时模式切换的稳定性集成控制策略。采用线性变参数实时简化模型，实现控制策略与车辆实时状态相匹配；针对路面参数等变化的重型半挂车复杂工况，利用适时模式切换机制，适时切换集成控制策略的控制模式，实现了各工况重型半挂车横摆、折叠和侧倾稳定性精准控制；采用遗传粒子群算法，设计了提高横摆、折叠和侧倾稳定性的各控制模式对应优化目标函数，优化了各控制模式的控制权重系数，融合协调了集成控制策略多个单一控制系统，实现了各控制模式重型半挂车最优控制。搭建了重型半挂车硬件在环试验台，并进行了所提控制策略试验台验证，结果表明: 本项目控制策略提升了危险工况的重型半挂车横摆和折叠稳定性和避免了极限工况的重型半挂车侧翻。而且，进行了多种控制策略对比研究，结果表明所提的集成控制策略优于差动制动控制策略和挂车主动转向控制策略。.鉴于此，本项目所提集成控制策略实现了山区高速公路复杂工况从普通工况到极限工况各工况稳定性精准最优控制，为山区高速公路复杂工况的重型半挂车稳定性集成控制提供了良好的理论基础和技术手段。