In this article, we address the simultaneous estimation problem of the lateral speed, the steering input, and the effective engine torque, which play a fundamental role in vehicle handling, stability control, and fault diagnosis of autonomous ground vehicles. Due to the involved longitudinal-lateral coupling dynamics and the presence of unknown inputs (UIs), a new nonlinear observer design technique is proposed to guarantee the asymptotic estimation performance. To this end, we make use of a specific Takagi–Sugeno (TS) fuzzy representation with nonlinear consequents to exactly model the nonlinear vehicle dynamics within a compact set of the vehicle state. This TS fuzzy modeling not only allows reducing significantly the real-time computational effort in estimating the vehicle variables but also enables an effective way to deal with unmeasured nonlinearities. Moreover, via a generalized Luenberger observer structure, the UI decoupling can be achieved without requiring a priori UI information. Using Lyapunov stability arguments, the UI observer design is reformulated as an optimization problem under linear matrix inequalities, which can be effectively solved with standard numerical solvers. The effectiveness of the proposed TS fuzzy UI observer design is demonstrated with real-time hardware-in-the-loop experiments.

中文翻译：

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Takagi-Sugeno Fuzzy Unknown Input Observers to Estimated Nonlinear Dynamics of Autonomous Ground Vehicles：理论和实时验证

在本文中，我们针对 横向速度、转向输入和有效发动机扭矩的同时估计问题，这些问题在自主地面车辆的车辆操纵、稳定性控制和故障诊断中起着基础性作用。由于涉及纵向-横向耦合动力学和未知输入（UI）的存在，提出了一种新的非线性观测器设计技术来保证渐近估计性能。为此，我们使用特定的 Takagi-Sugeno (TS) 模糊表示，其具有非线性结果在一组紧凑的车辆状态中精确地模拟非线性车辆动力学。这种 TS 模糊建模不仅可以显着减少估计车辆变量的实时计算工作量，而且可以有效地处理未测量的非线性。此外，通过一个广义的 Luenberger 观察者结构，无需要求即可实现 UI 解耦 先验 UI 信息。使用 Lyapunov 稳定性参数，UI 观察器设计被重新表述为线性矩阵不等式下的优化问题，可以使用标准数值求解器有效地求解。通过实时硬件在环实验证明了所提出的 TS 模糊 UI 观察器设计的有效性。