Fuzzy logic controller for autonomous vehicle path tracking

2017 ◽  
Author(s):  
Sami Allou ◽  
Youcef Zennir ◽  
Aissa Belmeguenai
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Vehicle Path

scholarly journals Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 128233-128249
Author(s):  
Mohammad Rokonuzzaman ◽  
Navid Mohajer ◽  
Saeid Nahavandi ◽  
Shady Mohamed
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Vehicle Path

Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation

2004 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Devendra P. Garg
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
Path Tracking ◽  
Mobile Robot Navigation ◽  
Reinforcement Learning Algorithm

This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.

A distributed fuzzy logic controller for an autonomous vehicle

2004 ◽  
Vol 21 (10) ◽  
pp. 499-516 ◽  
Author(s):  
Neil Eugene Hodge ◽  
Linda Zhixia Shi ◽  
Mohamed B. Trabia
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Autonomous Vehicle

Autonomous vehicle path tracking control considering the stability under lane change

2021 ◽  
pp. 095965182199135
Author(s):  
Xiaolong Chen ◽  
Bing Zhou ◽  
Xiaojian Wu
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Linear Time ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Steering Angle ◽  
Adhesion Coefficient ◽  
The Road ◽  
Vehicle Path

Considering that when a vehicle travels on a low friction coefficient road with high speed, the path tracking ability declines. To keep the performance of path tracking and improve the stabilization under that situation, this article presents approaches to estimate the parameters and control the vehicle. First, the key states of the vehicle and the road adhesion coefficient are estimated by the unscented Kalman filter. This is followed by applying the linear time-varying model-based predictive controller to achieve path tracking control, and the initial tire steering angle control rate is obtained. Finally, the steering angle compensation controller is simultaneously designed by a simple receding horizon corrector algorithm to improve vehicle stability when the path is tracked on a low-adhesion coefficient or at high speed. The performance of the proposed approach is evaluated by software CarSim and MATLAB/Simulink. Simulation results show that an improvement in the performance of path tracking and stabilization can be achieved by the integrated controller under the variable road adhesion coefficient condition and high speed with 110 km/h.

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