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。该问题自然存在于需要可靠系统辨识和基本反馈控制器的公路及越野车辆中，涉及车道保持、航点导航等多种应用场景。传统系统辨识方法需要昂贵设备且耗时巨大。本研究探索利用微分物理进行系统辨识与控制器设计，主要贡献包括：i) 开发微分物理仿真器（DPS），为前轮转向类车辆提供基于梯度方法的系统辨识方案，通过梯度法学习系统参数；ii) 证明所提梯度方法相较于其他无梯度方法具有更优的样本效率；iii) 通过在真实前轮转向车辆F1TENTH上实现反馈控制器验证所学系统参数，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与F1TENTH实际系统参数在车道保持行为上具有可比性。