Steer-by-wire (SBW) systems are considered as one of the most significant innovations among the technologies developed for advanced driver-assistance systems and autonomous vehicles. The main control challenge in a SBW system is to follow the steering commands in the face of parametric uncertainties and external disturbances; crucially, perturbations in inertial parameters and damping forces give rise to state-dependent uncertainties, which cannot be bounded a priori by a constant. However, the state-of-the-art control methods of SBW system rely on a priori bounded uncertainties, and thus, become inapplicable when state-dependent dynamics become unknown. This work, to the best of the authors' knowledge for the first time, proposes an adaptive control framework that can tackle the state-dependent uncertainties and external disturbances in a typical SBW system without any a priori knowledge of their structures and of their bounds. The stability of the closed-loop system is studied analytically via uniformly ultimately bounded notion and the effectiveness of the proposed solution is verified via simulations against the state-of-the-art solution.

中文翻译：

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未知状态相关不确定性下线控转向系统的鲁棒自适应控制

线控转向 (SBW) 系统被认为是为高级驾驶辅助系统和自动驾驶汽车开发的技术中最重要的创新之一。SBW 系统的主要控制挑战是在面对参数不确定性和外部干扰时遵循转向命令；至关重要的是，惯性参数和阻尼力的扰动会导致与状态相关的不确定性，这些不确定性不能先验地受到常数的限制。然而，SBW 系统的最新控制方法依赖于先验的有界不确定性，因此当状态相关动力学变得未知时变得不适用. 据作者所知，这项工作首次提出了一种自适应控制框架，该框架可以解决典型 SBW 系统中与状态相关的不确定性和外部干扰，而无需对其结构和边界有任何先验知识。通过统一最终有界概念对闭环系统的稳定性进行了分析研究，并通过针对最先进解决方案的模拟验证了所提出解决方案的有效性。