Adaptive fuzzy controller design for trajectory tracking of a 2 DOF wheeled mobile robot using genetic algorithm

2002 ◽  
Author(s):  
Sung-Hoe Kim ◽  
Chong-Kug Park ◽  
F. Harashima
Keyword(s):  
Genetic Algorithm ◽  
Mobile Robot ◽  
Trajectory Tracking ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Wheeled Mobile Robot ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

Adaptive fuzzy controller design for dynamic positioning ship integrating prescribed performance

2021 ◽  
Vol 219 ◽  
pp. 107956
Author(s):  
Yuanhui Wang ◽  
Haibin Wang ◽  
Mingyang Li ◽  
Duansong Wang ◽  
Mingyu Fu
Keyword(s):  
Fuzzy Controller ◽  
Controller Design ◽  
Dynamic Positioning ◽  
Adaptive Fuzzy Controller ◽  
Prescribed Performance ◽  
Adaptive Fuzzy

scholarly journals Backstepping Based Trajectory Tracking Control of a Four Wheeled Mobile Robot

10.5772/6224 ◽  
2008 ◽  
Vol 5 (4) ◽  
pp. 38 ◽  
Author(s):  
Umesh Kumar ◽  
Nagarajan Sukavanam
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Controller Design ◽  
Kinematic Model ◽  
Wheeled Mobile Robot ◽  
Trajectory Tracking Control ◽  
Kinematic Equation ◽  
Pure Rolling ◽  
Tracking Strategy

For a four wheeled mobile robot a trajectory tracking concept is developed based on its kinematics. A trajectory is a time–indexed path in the plane consisting of position and orientation. The mobile robot is modeled as a non holonomic system subject to pure rolling, no slip constraints. To facilitate the controller design the kinematic equation can be converted into chained form using some change of co-ordinates. From the kinematic model of the robot a backstepping based tracking controller is derived. Simulation results demonstrate such trajectory tracking strategy for the kinematics indeed gives rise to an effective methodology to follow the desired trajectory asymptotically.

Observer Based Adaptive Fuzzy Controller with Modulated Membership Functions for Nonlinear System

2016 ◽  
Vol 24 (01) ◽  
pp. 137-159 ◽  
Author(s):  
Leehter Yao ◽  
Jen-nan Jiang ◽  
Tung-bin Lin
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear System ◽  
Supervisory Control ◽  
Fuzzy Controller ◽  
Optimal Solutions ◽  
Membership Functions ◽  
Lyapunov Theorem ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
On Line

An observer based adaptive fuzzy controller for nonlinear system is proposed. Parameters in the proposed adaptive fuzzy controller are tuned on-line by the genetic algorithm (GA). For on-line tuning, a parsimonious parameterization scheme for fuzzy controller called orthogonal modulated membership functions (mmf)35 is utilized. A simplified GA called micro GA that greatly improves the learning efficiency is applied. The valid range of mmf parameters will be proved in this paper. With the valid range of mmf parameters, the search by MGA for the optimal parameterization of the adaptive fuzzy controller is more focused resulting in fast convergence to the optimal solutions. A Lyapunov theorem based supervisory control is added to the fuzzy controller assuring that close loop stability is always maintained for the adaptive fuzzy controller during the learning process.

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