论文标题
概率迭代LQR在短时间内MPC
Probabilistic Iterative LQR for Short Time Horizon MPC
论文作者
论文摘要
最佳控制通常用于机器人技术中，以规划轨迹以实现某些所需的行为，如成本函数所表达的那样。大多数在最佳控制中的作用集中在查找单个最佳轨迹上，然后通常由另一个控制器跟踪。在这项工作中，我们将轨迹分布视为最佳控制问题的解决方案，从而可以更好地跟踪性能和更稳定的控制器。首先是从迭代线性二次调节器（ILQR）求解器获得的高斯分布。然后，使用短范围模型预测控制（MPC）来跟踪此分布。我们表明，与跟踪平均值或使用ILQR反馈控制相比，跟踪分布更具成本效益和健壮。该方法通过对7-DOF PANDA机械手的运动学控制和模拟中的6-DOF四轮驱动器的动态控制验证。
Optimal control is often used in robotics for planning a trajectory to achieve some desired behavior, as expressed by the cost function. Most works in optimal control focus on finding a single optimal trajectory, which is then typically tracked by another controller. In this work, we instead consider trajectory distribution as the solution of an optimal control problem, resulting in better tracking performance and a more stable controller. A Gaussian distribution is first obtained from an iterative Linear Quadratic Regulator (iLQR) solver. A short horizon Model Predictive Control (MPC) is then used to track this distribution. We show that tracking the distribution is more cost-efficient and robust as compared to tracking the mean or using iLQR feedback control. The proposed method is validated with kinematic control of 7-DoF Panda manipulator and dynamic control of 6-DoF quadcopter in simulation.
