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Data-Driven Shared Steering Control of Semi-Autonomous Vehicles

机译:半自动车辆的数据驱动共享转向控制

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摘要

This paper presents a cooperative/shared framework of the driver and his/her semi-autonomous vehicle in order to achieve desired steering performance. In particular, a copilot controller and the driver together operate and control the vehicle. Exploiting the classical small-gain theory, our proposed shared steering controller is developed independent of the unmeasurable internal states of the human driver, and only relies on his/her steering torque. Furthermore, by adopting data-driven adaptive dynamic programming and an iterative learning scheme, the shared steering controller is studied from the measurable data of the driver and the vehicle. Meanwhile, the accurate knowledge of the driver and the vehicle dynamics is unnecessary, which settles the problem of their potential parametric variations/uncertainties in practice. The effectiveness of the proposed method is validated by rigorous analysis and demonstrated by numerical simulations.
机译:本文提出了驾驶员和他/她的半自动车辆的合作/共享框架,以实现所需的转向性能。特别地,副驾驶员控制器和驾驶员一起操作和控制车辆。利用经典的小增益理论,我们提出的共享转向控制器的开发独立于驾驶员无法测量的内部状态,并且仅依赖于驾驶员的转向扭矩。此外,通过采用数据驱动的自适应动态规划和迭代学习方案,从驾驶员和车辆的可测量数据中研究共享转向控制器。同时,不需要驾驶员和车辆动力学的准确知识,这解决了它们在实践中潜在的参数变化/不确定性的问题。通过严格的分析验证了该方法的有效性,并通过数值模拟证明了该方法的有效性。

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