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Takagi-Sugeno Model-based Steering Control for Autonomous Vehicles with Actuator Saturation *

机译：基于Takagi-Sugeno模型的执行器饱和自动驾驶汽车的转向控制 *

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This paper is devoted to the development of a novel control law for an autonomous vehicle. The control goal is to perform lane keeping under multiple constraints, namely actuator saturation of the steering system, roads with unknown curvature, and uncertain lateral wind force. Such system constraints will be explicitly considered in the control design procedure. To achieve this goal, we propose a novel constrained Takagi-Sugeno control method using non-quadratic Lyapunov stability framework. The obtained non-parallel distributed compensation controller is not only able to handle various system constraints but also a large variation range of vehicle speed. Especially, by means of the well-known Taylor’s approximation method we can significantly reduce the numerical complexity of the proposed control method to ease the real-time control implementation. The control synthesis conditions are expressed in terms of linear matrix inequalities which can be efficiently solved with available solvers. The effectiveness of the proposed method is demonstrated with different simulation scenarios.
机译：本文致力于自动驾驶汽车新型控制律的发展。控制目标是在多个约束条件下执行车道保持，即转向系统的执行器饱和，曲率未知的道路和侧向风力不确定的道路。这样的系统约束将在控制设计过程中明确考虑。为了实现这一目标，我们提出了一种使用非二次Lyapunov稳定性框架的新型约束Takagi-Sugeno控制方法。所获得的非并行分布补偿控制器不仅能够应对各种系统约束，而且能够应对较大的车速变化范围。尤其是，借助众所周知的泰勒近似方法，我们可以显着降低所提出的控制方法的数值复杂度，从而简化实时控制的实现。控制合成条件以线性矩阵不等式表示，可以使用可用的求解器进行有效求解。在不同的仿真场景下证明了该方法的有效性。

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