论文标题
ABC-LMPC：具有可调边界条件的随机非线性动力学系统的基于安全样本的学习MPC
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions
论文作者
论文摘要
基于样本的学习模型预测控制（LMPC）策略最近由于其理想的理论特性及其在机器人任务上的良好经验表现而引起了人们的关注。但是，对随机系统的LMPC控制器的先前分析主要集中在迭代学习控制设置中的线性系统上。我们提出了一种新颖的LMPC算法，可调节的边界条件LMPC（ABC-LMPC），该算法可以快速适应新颖的开始和目标配置，并且从理论上表明，所得的控制器可以保证对随机非线性系统的期望进行迭代改善。我们通过对该算法的实际实例化提出了结果，并在实验上证明，所得控制器适应了3个随机连续控制任务的各种初始和终端条件。
Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performance on robotic tasks. However, prior analysis of LMPC controllers for stochastic systems has mainly focused on linear systems in the iterative learning control setting. We present a novel LMPC algorithm, Adjustable Boundary Condition LMPC (ABC-LMPC), which enables rapid adaptation to novel start and goal configurations and theoretically show that the resulting controller guarantees iterative improvement in expectation for stochastic nonlinear systems. We present results with a practical instantiation of this algorithm and experimentally demonstrate that the resulting controller adapts to a variety of initial and terminal conditions on 3 stochastic continuous control tasks.
