基于快速层位管的自主车辆LPV-MPC
Fast Zonotope-Tube-based LPV-MPC for Autonomous Vehicles
摘要：
在本文中，我们提出了一个有效的在线管为基础的模型预测控制（T-MPC）解决方案的自动驾驶，旨在提高计算负荷，同时确保鲁棒稳定性和性能在快速和干扰的情况下。通过将非线性车辆方程转化为线性变参数（LPV）形式，将非线性原问题转化为伪线性问题。提出了一种由标称控制器和校正局部控制器组成的控制方案。首先，局部控制器被设计为能够抑制外部干扰的多面体LPV-H $_{\infty}$控制器。此外，有限数量的精确可达集，也称为管，计算在线使用zonotopes考虑到系统的动态，本地控制器和diturbance不确定性的界限。其次，名义控制器被设计为MPC，其中LPV车辆模型用于加快计算时间，同时保持准确的车辆表示。该模型采用带拓扑的可达性理论，在线改变状态和输入约束，保证了模型在外生扰动下的鲁棒可行性和稳定性。最后，我们测试了所提出的方案，并将局部控制器性能与最先进的LQR设计进行了比较。我们证明了它的有效性，在一个受干扰的快速驾驶的情况下，能够拒绝强烈的外源性干扰，并以非常低的计算成本实现强加的约束。
英文摘要：
In this paper, we present an effective online tube-based model predictive control (T-MPC) solution for autonomous driving that aims at improving the computational load while ensuring robust stability and performance in fast and disturbed scenarios. We focus on reformulating the non-linear original problem into a pseudo-linear problem by transforming the non-linear vehicle equations to be expressed in a Linear Parameter Varying (LPV) form. An scheme composed by a nominal controller and a corrective local controller is propossed. First, the local controller is designed as a polytopic LPV-H$_{\infty}$ controller able to reject external disturbances. Moreover, a finite number of accurate reachable sets, also called tube, are computed online using zonotopes taking into account the system dynamics, the local controller and the diturbance-uncertainty bounds considered. Second, the nominal controller is designed as an MPC where the LPV vehicle model is used to speed up the computational time while keeping accurate vehicle representation. Employing reachability theory with zonotopes, the MPC changes online its state and input constraints to ensure robust feasibility and stability under exhogenous disturbances. Finally, we test the presented scheme and compare the local controller performance against the LQR design as state of the art approach. We demonstrate its effectiveness in a disturbed fast driving scenario being able to reject strong exogenous disturbances and fulfilling imposed constraints at a very reduced computational cost.
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