In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

中文翻译：

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使用改进的差分搜索和强化学习的模糊逻辑控制器的移动机器人跟踪控制

为了解决机器人跟踪控制问题，提出了一种带有改进改进差分搜索算法（FLC_R-IDS）的模糊逻辑控制器。本研究利用强化学习的奖惩机制来训练移动机器人的墙跟控制。所提出的改进的差分搜索算法使用参数自适应来调整控制参数。为了改善算法的探索性，需要更改超微生物的数量，因为它涉及到中途停留的地点。本研究使用强化学习来指导机器人的行为。当移动机器人满足三个奖励条件时，它将获得奖励+1。累积的奖励值用于评估控制器并替换下一次控制器培训。实验结果表明，与传统的差分搜索算法和混沌差分搜索算法相比，所提出的FLC_R-IDS在三个实验环境中的平均误差值分别降低了12.44％，22.54％和25.98％。最终，实验结果还表明，使用所提出的方法的真实移动机器人可以有效地实现跟踪墙的控制。