In this paper, we address the problem of system identification and control of a front-steered vehicle which abides by the Ackermann geometry constraints. This problem arises naturally for on-road and off-road vehicles that require reliable system identification and basic feedback controllers for various applications such as lane keeping and way-point navigation. Traditional system identification requires expensive equipment and is time consuming. In this work we explore the use of differentiable physics for system identification and controller design and make the following contributions: i)We develop a differentiable physics simulator (DPS) to provide a method for the system identification of front-steered class of vehicles whose system parameters are learned using a gradient-based method; ii) We provide results for our gradient-based method that exhibit better sample efficiency in comparison to other gradient-free methods; iii) We validate the learned system parameters by implementing a feedback controller to demonstrate stable lane keeping performance on a real front-steered vehicle, the F1TENTH; iv) Further, we provide results exhibiting comparable lane keeping behavior for system parameters learned using our gradient-based method with lane keeping behavior of the actual system parameters of the F1TENTH.

翻译：本文针对遵循阿克曼几何约束的前轮转向车辆的系统辨识与控制问题展开研究。这一问题在道路与非道路车辆中自然产生，此类车辆需要可靠的系统辨识及基础反馈控制器以支持车道保持、航点导航等应用。传统系统辨识方法依赖昂贵设备且耗时。本文探索利用可微物理进行系统辨识与控制器设计，作出以下贡献：i) 构建可微物理仿真器(DPS)，提出基于梯度方法的前轮转向类车辆系统辨识方法，通过梯度优化学习系统参数；ii) 实验表明相较于无梯度方法，本文梯度方法具有更优的样本效率；iii) 通过在实际前轮转向车辆F1TENTH上实施反馈控制器验证学习所得系统参数，展示稳定的车道保持性能；iv) 进一步证明，基于梯度方法学习的系统参数在车道保持行为上与F1TENTH实际系统参数具有可比性。