Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Abstract

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.

Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control

“…Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control. Relative to our previous published papers [24][25][26], the major contributions of this study are as follows: (1) an efficient KNFC is proposed for mobile robot navigation control, (2) the proposed KCMDE algorithm contains features of both the CA and DE strategy, and is implemented using the knowledge sources of the belief space in the CA to increase global search ability, and (3) in special environments, two thresholds are used to switch the controller mode between the wall-following mode and the general controller.…”

“…This section introduces the experiments conducted using the mobile robot PIONEER 3-DX [24][25][26] (see Figure 1). The robot includes eight front ultrasonic sensors, a battery, two differential drive wheels, and wheel encoders.…”

“…In this study, the detection range of the ultrasonic sensors was approximately 20-70 cm. The configuration of the position of the ultrasonic sensors [24][25][26] The eight ultrasonic sensors (sensor1, sensor2, …, sensor8) were allocated into three call signs: S1, S2, and S3. S1 is the distance between the obstacle and left sensor of the robot, S2 is the distance between the obstacle and the front sensor of the robot, and S3 is the distance between the obstacle and right sensor of the robot.…”

“…The other two parameters, cj and dj, are the internal parameters of γj, where γj is the compensatory factor of the KNFC. The coding method [24] is illustrated in Figure 5. The population of the KCMDE algorithm is generated according to the coding method.…”

Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation

“…Fuzzy logic control (FLC) is a structure that mimics human knowledge or action based on linguistic rules that are tuned according to the designer [28][29][30]; these type of controllers have been used in applications for maximum power point tracking, such as fuel cells and photo-voltaic systems [31,32], electrical drivers [33], etc. The author of [34] indicated that FLC that is based on PID grants a high accuracy for PEAs guidancem, but it can also provide other advantages over alternative control structures.…”