Intelligent vehicle path tracking coordinated optimization based on dual-steering cooperative game with fault-tolerant function

IF 4.4 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Hongbo Wang, Wenjie Zheng, Juntao Zhou, Lizhao Feng, Haiping Du
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Abstract

Since that a single controller for intelligent vehicle path tracking is difficult to adapt to complex working conditions and achieve good system control performance, inspired by the concept of control redundancy, an adaptive weight based dual-steering control cooperative game coordinated optimization with fault-tolerant control method for intelligent vehicle path tracking is proposed in this paper. Firstly, the three-degree-of-freedom of lateral, yaw and longitudinal directions is considered to establish the vehicle single-track dynamics model, and two model predictive path tracking steering controllers with different control objective emphases in tracking accuracy and yaw stability are designed. Then, the cooperative game theory is introduced to model the steering interaction of dual controllers, and its Pareto equilibrium optimal solution is solved by the distributed model predictive control framework. According to the influence trend of two game players’ relative participation weights on the path tracking results, an adaptive weight adjustment strategy based on predicted output deviation is designed, to achieve the coordinated optimization of dual steering control, and the steering fault-tolerant control is also realized by cooperative game in some cases of steering partial failure. Carsim/Simulink co-simulation and hardware-in-loop test under the variable curvature path and control failure conditions prove that by the proposed method intelligent vehicle path tracking dual steering control can be coordinated optimized and optimal path tracking performance is obtained, and demonstrate the effectiveness of the adaptive weight adjustment method which has better fault-tolerant control compared to fixed weight allocation under different degrees of steering output failures.
基于具有容错功能的双转向合作博弈的智能车辆路径跟踪协调优化
由于单一的智能车辆路径跟踪控制器难以适应复杂工况并实现良好的系统控制性能,受控制冗余概念的启发,本文提出了一种基于自适应权重的双转向控制协同博弈协调优化容错控制方法,用于智能车辆路径跟踪。首先,考虑横向、偏航和纵向三自由度,建立了车辆单轨动力学模型,并设计了两个在跟踪精度和偏航稳定性方面具有不同控制目标侧重点的模型预测路径跟踪转向控制器。然后,引入合作博弈论建立双控制器转向交互模型,并通过分布式模型预测控制框架求解其帕累托均衡最优解。根据两个博弈方相对参与权重对路径跟踪结果的影响趋势,设计了基于预测输出偏差的自适应权重调整策略,实现了双转向控制的协调优化,并在某些转向部分失效的情况下通过合作博弈实现了转向容错控制。在变曲率路径和控制失效条件下进行的 Carsim/Simulink 协同仿真和硬件在环测试证明,采用所提出的方法可以协调优化智能车辆路径跟踪双转向控制,获得最佳路径跟踪性能,并证明了自适应权重调整方法的有效性,在不同程度的转向输出失效情况下,该方法与固定权重分配相比具有更好的容错控制能力。
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来源期刊
Applied Mathematical Modelling
Applied Mathematical Modelling 数学-工程:综合
CiteScore
9.80
自引率
8.00%
发文量
508
审稿时长
43 days
期刊介绍: Applied Mathematical Modelling focuses on research related to the mathematical modelling of engineering and environmental processes, manufacturing, and industrial systems. A significant emerging area of research activity involves multiphysics processes, and contributions in this area are particularly encouraged. This influential publication covers a wide spectrum of subjects including heat transfer, fluid mechanics, CFD, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and MHD; reliability modelling and system optimization; finite volume, finite element, and boundary element procedures; modelling of inventory, industrial, manufacturing and logistics systems for viable decision making; civil engineering systems and structures; mineral and energy resources; relevant software engineering issues associated with CAD and CAE; and materials and metallurgical engineering. Applied Mathematical Modelling is primarily interested in papers developing increased insights into real-world problems through novel mathematical modelling, novel applications or a combination of these. Papers employing existing numerical techniques must demonstrate sufficient novelty in the solution of practical problems. Papers on fuzzy logic in decision-making or purely financial mathematics are normally not considered. Research on fractional differential equations, bifurcation, and numerical methods needs to include practical examples. Population dynamics must solve realistic scenarios. Papers in the area of logistics and business modelling should demonstrate meaningful managerial insight. Submissions with no real-world application will not be considered.
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