In this paper, a synthesized novel strategy of varying predictive horizon-based model predictive control is proposed for the overtaking control of unmanned ground vehicle. The whole control strategy includes path planning and path tracking. First, the preferred path in presence of diverse constraints of states, inputs, and collision avoidance can be calculated using Gauss pseudospectral method where expected position, velocity, and attitude are provided. Correspondingly, the continuous optimal control problem is converted to discrete nonlinear programming. Second, model predictive control is developed for tracking the optimized path. Considering the effect of the predictive horizon and the Gauss points’ distribution on tracking performance, the varying predictive horizon is introduced to improve the tracking accuracy in non-smooth path. By the varying predictive horizon-based model predictive control method, less computation burden and better control performance are achieved. For the difference between the mathematical expressions and the real unmanned ground vehicle dynamics, genetic algorithm is utilized to identify the parameters of tire model. Simulations in MATLAB and CarSim are both implemented to illustrate the effectiveness of the proposed method.

中文翻译：

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基于模型预测控制的无人地面车辆变视野超车控制策略

在本文中，针对无人地面车辆的超车控制，提出了一种基于变化预测水平的模型预测控制的综合新策略。整个控制策略包括路径规划和路径跟踪。首先，可以使用提供预期位置、速度和姿态的高斯伪谱方法计算存在各种状态、输入和碰撞避免约束的首选路径。相应地，连续最优控制问题转化为离散非线性规划。其次，开发模型预测控制以跟踪优化路径。考虑预测范围和高斯点分布对跟踪性能的影响，引入变化的预测范围来提高非光滑路径的跟踪精度。通过基于变化预测范围的模型预测控制方法，实现了更少的计算负担和更好的控制性能。针对数学表达式与真实无人地面车辆动力学的差异，采用遗传算法对轮胎模型参数进行识别。MATLAB 和 CarSim 中的仿真都被实现以说明所提出方法的有效性。