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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Autonomous Ground Vehicle Lane-Keeping LPV Model-Based Control: Dual-Rate State Estimation and Comparison of Different Real-Time Control Strategies
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Autonomous Ground Vehicle Lane-Keeping LPV Model-Based Control: Dual-Rate State Estimation and Comparison of Different Real-Time Control Strategies

机译：自主地面车道保持LPV模型的控制：双速率状态估计和不同实时控制策略的比较

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In this contribution, we suggest two proposals to achieve fast, real-time lane-keeping control for Autonomous Ground Vehicles (AGVs). The goal of lane-keeping is to orient and keep the vehicle within a given reference path using the front wheel steering angle as the control action for a specific longitudinal velocity. While nonlinear models can describe the lateral dynamics of the vehicle in an accurate manner, they might lead to difficulties when computing some control laws such as Model Predictive Control (MPC) in real time. Therefore, our first proposal is to use a Linear Parameter Varying (LPV) model to describe the AGV’s lateral dynamics, as a trade-off between computational complexity and model accuracy. Additionally, AGV sensors typically work at different measurement acquisition frequencies so that Kalman Filters (KFs) are usually needed for sensor fusion. Our second proposal is to use a Dual-Rate Extended Kalman Filter (DREFKF) to alleviate the cost of updating the internal state of the filter. To check the validity of our proposals, an LPV model-based control strategy is compared in simulations over a circuit path to another reduced computational complexity control strategy, the Inverse Kinematic Bicycle model (IKIBI), in the presence of process and measurement Gaussian noise. The LPV-MPC controller is shown to provide a more accurate lane-keeping behavior than an IKIBI control strategy. Finally, it is seen that Dual-Rate Extended Kalman Filters (DREKFs) constitute an interesting tool for providing fast vehicle state estimation in an AGV lane-keeping application.
机译：在这一贡献中，我们建议为自主地面车辆（AGVS）实现快速，实时车道控制的两项建议。泳道保持的目标是通过前轮转向角作为特定纵向速度的控制动作来定向和将车辆保持在给定参考路径内。虽然非线性模型可以以准确的方式描述车辆的横向动态，但在实时计算诸如模型预测控制（MPC）之类的控制规律时，它们可能会导致困难。因此，我们的第一个提议是使用线性参数变化（LPV）模型来描述AGV的横向动态，作为计算复杂性和模型精度之间的权衡。另外，AGV传感器通常在不同的测量采集频率下工作，以便通常需要卡尔曼滤波器（KFS）进行传感器融合。我们的第二个提案是使用双重速率扩展卡尔曼滤波器（DREFKF）来缓解更新过滤器内部状态的成本。为了检查我们提案的有效性，将基于LPV模型的控制策略进行了比较，以在处理过程和测量高斯噪声的情况下，对另一个降低的计算复杂性控制策略（ICKIBI）的电路路径进行了模拟。 LPV-MPC控制器显示为提供比Ikibi控制策略更准确的道路保持行为。最后，可以看出，双重速率扩展卡尔曼滤波器（DREKFS）构成了用于在AGV泳道保存应用中提供快速车辆状态估计的有趣工具。

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