Artificial Fuzzy‐PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms

Abstract

Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency

“…The developed platform improved the process of design modifications and contributed to a solution of the motion control problem in terms of evaluating the designed control algorithm in its attainment of the desired output motion characteristics. Based on the outcome, sufficient and robust results in path tracking were produced, confirming the benefit of the combined fuzzy and PID control strategy [16].…”

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

“…ANFIS, fuzzy, and PID speed controllers for wheeled mobile robots are developed and compared in Khan et al (2022). Yousfi et al (2021) proposed a hybrid fuzzy logic PID based dynamic model controller to assure target achievement and trajectory tracking. Yousfi et al (2021) offered two control strategies for non-holonomic mobile robots evolving in environments with multiple external effects, one based on a PD fuzzy logic controller and the other on a smart PID optimized neural networks-based controller.…”

“…Yousfi et al (2021) proposed a hybrid fuzzy logic PID based dynamic model controller to assure target achievement and trajectory tracking. Yousfi et al (2021) offered two control strategies for non-holonomic mobile robots evolving in environments with multiple external effects, one based on a PD fuzzy logic controller and the other on a smart PID optimized neural networks-based controller. A non-holonomic autonomous wheeled robot is proposed by Ben Jabeur and Seddik (2021) that follows a predefined path using a combination of back stepping and an adaptive fuzzy PID approach.…”