论文标题
使用神经网络动力学模型的迭代LQR控制器，用于越野和道路车辆
An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model
论文作者
论文摘要
在这项工作中，我们评估了迭代线性二次调节器（ILQR）的轨迹跟踪两种不同类型的轮式移动机器人，即疣猪（图1），这是一种越野自变量机器人，带有slkid steeering和Polaris Gem E6 [1] [1] [1]，这是一种非全面的六层赛车（图2）。我们使用多层神经网络来学习这些机器人的离散动态模型，该模型在ILQR控制器中用于计算控制法。我们使用模型预测控制（MPC）来处理模型缺陷并执行广泛的实验，以评估Warthog的3m/s-4m/s的人体驱动参考轨迹的性能，对于Polaris Gem
In this work we evaluate Iterative Linear Quadratic Regulator(ILQR) for trajectory tracking of two different kinds of wheeled mobile robots namely Warthog (Fig. 1), an off-road holonomic robot with skid-steering and Polaris GEM e6 [1], a non-holonomic six seater vehicle (Fig. 2). We use multilayer neural network to learn the discrete dynamic model of these robots which is used in ILQR controller to compute the control law. We use model predictive control (MPC) to deal with model imperfections and perform extensive experiments to evaluate the performance of the controller on human driven reference trajectories with vehicle speeds of 3m/s- 4m/s for warthog and 7m/s-10m/s for the Polaris GEM
