论文标题
基于学习的预测路径遵循不确定干扰的非线性系统的控制
Learning-based Predictive Path Following Control for Nonlinear Systems Under Uncertain Disturbances
论文作者
论文摘要
准确的路径以下是在不确定环境中运行的自主机器人具有挑战性的。自适应和预测控制策略对于非线性机器人系统至关重要，以在控制后达到高性能路径。在本文中，我们提出了一种新型的基于学习的预测控制方案，该方案将在不确定干扰下的非线性系统中与基于低级学习的反馈线性化控制器（LB-FBLC）一起，将高级模型预测路径（MPFC）与低级学习的反馈线性化控制器（LB-FBLC）结合在一起。低级LB-FBLC利用高斯流程在线学习不确定的环境干扰，并通过概率稳定性保证准确跟踪参考状态。同时，高级MPFC利用了使用虚拟线性路径动力学模型增强线性化系统模型，以优化路径参考目标的演变，并为低级LB-FBLC提供参考状态和控件。仿真结果说明了在未知的风干扰下任务后，提出的控制策略在四轨路径上的有效性。
Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.
