Design and Implementation of a Takagi–Sugeno-Type Fuzzy Logic Controller on a Two-Wheeled Mobile Robot

Autonomous path following in mobile robots with nonholonomic constraints can be divided into two problems: first, selecting the tracking point, and then designing an appropriate controller to follow the selected point. When selecting tracking point, considering the kinematics of the robot as well as characteristics of the desired path is of considerable importance. For these purposes, a curvature-based point selection algorithm is first proposed for the car-like mobile robot with independent steering mechanisms. Each instant, the proposed algorithm finds a point which enables the robot to be tangent to the path at that specific point. Afterwards, in order to take into consideration characteristics of the path, a fuzzy adaptive curvature-based point selection algorithm is proposed. In this algorithm, in addition to the kinematic constraints, path characteristics are also considered in selecting the tracking point. This gives the robot the ability to show better performance when the path slope changes suddenly, resulting in less overshoot/undershoot around the desired path. The fuzzy adaptive curvature-based point selection algorithm is combined with a controller based on the Takagi–Sugeno fuzzy logic, such that the fuzzy adaptive curvature-based point selection algorithm selects the tracking point, while the Takagi–Sugeno fuzzy logic controller makes the robot follow the selected point. Finally, the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic tracker is implemented on the robot, and the results are compared with a similar path-following algorithm. Obtained results show that for tracking a piecewise linear path, the steering activity and the following root mean square error decrease from 170.74° and 0.37 m for the conventional fuzzy controller to 63.37° and 0.09 m for the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic controller, respectively.

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Parameter tuning of membership functions of a fuzzy logic controller for an autonomous wheeled mobile robot using ant colony optimization

2009 IEEE International Conference on Fuzzy Systems ◽

10.1109/fuzzy.2009.5277290 ◽

2009 ◽

Cited By ~ 3

Author(s):

R. Martinez-Marroquin ◽

O. Castillo ◽

J. Soria

Keyword(s):

Fuzzy Logic ◽

Mobile Robot ◽

Ant Colony Optimization ◽

Fuzzy Logic Controller ◽

Parameter Tuning ◽

Wheeled Mobile Robot ◽

Ant Colony ◽

Membership Functions

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Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot

Applied Sciences ◽

10.3390/app11136023 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6023

Author(s):

Alexandr Štefek ◽

Van Thuan Pham ◽

Vaclav Krivanek ◽

Khac Lam Pham

Keyword(s):

Fuzzy Logic ◽

Energy Consumption ◽

Mobile Robot ◽

Motion Control ◽

Fuzzy Logic Controller ◽

Operation Time ◽

Wheeled Mobile Robot ◽

Optimization Process ◽

Differential Drive ◽

Full Optimization

The energy-efficient motion control of a mobile robot fueled by batteries is an especially important and difficult problem, which needs to be continually addressed in order to prolong the robot’s independent operation time. Thus, in this article, a full optimization process for a fuzzy logic controller (FLC) is proposed. The optimization process employs a genetic algorithm (GA) to minimize the energy consumption of a differential drive wheeled mobile robot (DDWMR) and still ensure its other performances of the motion control. The earlier approaches mainly focused on energy reduction by planning the shortest path whereas this approach aims to optimize the controller for minimizing acceleration of the robot during point-to-point movement and thus minimize the energy consumption. The proposed optimized controller is based on fuzzy logic systems. At first, an FLC has been designed based on the experiment and as well as an experience to navigate the DDWMR to a known destination by following the given path. Next, a full optimization process by using the GA is operated to automatically generate the best parameters of all membership functions for the FLC. To evaluate its effectiveness, a set of other well-known controllers have been implemented in Google Colab® and Jupyter platforms in Python language to compare them with each other. The simulation results have shown that about 110% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternative controllers. Also, this simulation program has been published as an open-source code for all readers who want to continue in the research.

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