Policy iteration-based integral reinforcement learning for online adaptive trajectory tracking of mobile robot

Tatsuki Ashida, H. Ichihara
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Abstract

This paper considers trajectory tracking control for a nonholonomic mobile robot using integral reinforcement learning (IRL) based on a value functional represented by integrating a local cost. The tracking error dynamics between the robot and reference trajectories takes the form of time-invariant input-affine continuous-time nonlinear systems if the reference trajectory counterpart of the translational and angular velocities are constant. This paper applies integral reinforcement learning to the tracking error dynamics by approximating the value functional from the data collected along the robot trajectory. The paper proposes a specific procedure to implement the IRL-based policy iteration online, including a batch least-squares minimization. The approximate value function updates the control policy to compensate for the translational and angular velocities that drive the robot. Numerical examples illustrate to demonstrate the tracking performance of integral reinforcement learning.
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基于策略迭代的移动机器人在线自适应轨迹跟踪积分强化学习
本文研究了一种基于积分函数的非完整移动机器人的轨迹跟踪控制方法。当参考轨迹对应的平动速度和角速度一定时,机器人与参考轨迹之间的跟踪误差动力学表现为定常输入-仿射连续非线性系统。本文将积分强化学习应用于跟踪误差动力学,通过逼近沿机器人轨迹收集的数据的值泛函。本文提出了一种实现基于irl的在线策略迭代的具体方法,包括批量最小二乘最小化。近似值函数更新控制策略以补偿驱动机器人的平动速度和角速度。数值例子说明了积分强化学习的跟踪性能。
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