基于数据驱动的鲁棒反步自适应巡航控制

为了实现高精度的鲁棒自适应巡航控制（ACC），提出基于数据驱动的鲁棒反步自适应巡航控制算法. 利用反步技术设计虚拟控制器，将车辆间距控制转化为速度控制，避免速度相关型间距策略带来的间距与速度控制耦合；构建基于数据的耦合滑模面并设计状态观测器，补偿车辆复杂的非线性动力学特性、离散误差及外部干扰，提升控制算法的鲁棒性；利用反馈控制及鲁棒控制技术设计数据驱动的ACC鲁棒控制算法；分别选取固定时间间距、变时间间距策略，利用所提ACC算法及基于比例积分（PI）的ACC算法进行车辆自适应巡航控制对比仿真验证. 对比实验结果表明，所提算法在控制精度、鲁棒性方面具有优越性.

Robust backstepping adaptive cruise control based on data-driven

A data-driven robust backstepping adaptive cruise control (ACC) algorithm was proposed to achieve high-precision robust ACC. First of all, a virtual controller was designed to convert the vehicle spacing control into speed control by using the backstepping technology, avoiding the coupling of speed and spacing control brought by the speed-dependent spacing strategy. Secondly, a data-based coupling sliding mode surface and state observer were constructed to compensate for the vehicle’s complex nonlinear dynamics, the discrete errors and the external disturbances, and the robustness of the control algorithm was improved. Then, a data-driven robust ACC algorithm was designed using feedback control and robust control techniques. Finally, the proposed ACC algorithm and the PI-based ACC were used for vehicle adaptive cruise tracking simulation verification with two spacing strategies (constant time headway and varying time headway). The superiority of the designed algorithm in terms of control accuracy and robustness are shown by the experimental results.