Fuzzy control method for a steering system consisting of a four-wheel individual steering and four-wheel individual drive electric chassis

2016 ◽  
Vol 31 (6) ◽  
pp. 2941-2948 ◽  
Author(s):  
Shujie Song ◽  
Jiwei Qu ◽  
Yining Li ◽  
Wei Zhou ◽  
Kangquan Guo
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Steering System

Study on Multimode Intelligent Control for Ship Steering System

2011 ◽  
Vol 58-60 ◽  
pp. 2621-2633
Author(s):  
Ming Hui Wang ◽  
Yong Quan Yu ◽  
Bi Zeng
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Intelligent Control ◽  
Control Method ◽  
Control Process ◽  
Steering System ◽  
Learning Ability ◽  
Effective Control ◽  
Control Effect ◽  
Ship Steering

The ship motion is characterized by nonlinearity, time varying, uncertainty and complex interference from the environment, therefore there are certain limits in conventional PID control and self-adapting control for ship steering system. This paper combines three intelligent control technologies, that is, fuzzy control, neural network and extension control, to propose a multimode intelligent control method. Fuzzy control is utilized to solve control problem of uncertainty system, and learning ability of neural network is utilized to optimize the controller parameters. A new multi-mode transition controller based on extension control is presented and well designed in this paper, which may realize smooth switching during control process. In order to satisfy the requirements of higher accuracy and faster response of complex system, every control strategy designed can realize ideal control effect within the scope of its effective control. The simulation experiment is made to test dynamic and static performances of ship steering system under model parameter perturbation and wave interference. The simulation results show that the control system achieves satisfactory performances by implementing the multimode intelligent control.

scholarly journals Research and application of smart streetlamp based on fuzzy control method

2021 ◽  
Vol 183 ◽  
pp. 341-348
Author(s):  
Maohua Ai ◽  
Pengju Wang ◽  
Wei Ma
Keyword(s):  
Fuzzy Control ◽  
Control Method

Modelling and control of manipulators with flexible links working on land and underwater environments

Robotica ◽  
2010 ◽  
Vol 29 (3) ◽  
pp. 461-470 ◽  
Author(s):  
Levent Gümüşel ◽  
Nurhan Gürsel Özmen
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Robot Manipulator ◽  
Control Methods ◽  
Control Laws ◽  
Linear Ordinary Differential Equations ◽  
Intelligent Technique ◽  
Wide Range ◽  
Classical Control ◽  
And Control

SUMMARYIn this study, modelling and control of a two-link robot manipulator whose first link is rigid and the second one is flexible is considered for both land and underwater conditions. Governing equations of the systems are derived from Hamilton's Principle and differential eigenvalue problem. A computer program is developed to solve non-linear ordinary differential equations defining the system dynamics by using Runge–Kutta algorithm. The response of the system is evaluated and compared by applying classical control methods; proportional control and proportional + derivative (PD) control and an intelligent technique; integral augmented fuzzy control method. Modelling of drag torques applied to the manipulators moving horizontally under the water is presented. The study confirmed the success of the proposed integral augmented fuzzy control laws as well as classical control methods to drive flexible robots in a wide range of working envelope without overshoot compared to the classical controls.

Energy Management System Optimal Strategy for Pure Electric Vehicle Based on the Dynamic Programming

2014 ◽  
Vol 556-562 ◽  
pp. 1472-1475 ◽  
Author(s):  
Bing Dong ◽  
Yan Tao Tian ◽  
Chang Jiu Zhou
Keyword(s):  
Dynamic Programming ◽  
Fuzzy Control ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management System ◽  
Control Method ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Energy Management System ◽  
Fuzzy Control Strategy

This thesis puts forward one optimal adaptive fuzzy control method based on the pure electric vehicle energy management system of the fuzzy control which has been founded already. By adding an optimizing researching model based on the conventional fuzzy control strategy, the thesis can pick up the valuable control rules based on the dynamic programming theory and also can adjust the parameter of the fuzzy controller automatically according to the system operating. These can make the sum of the energy loss reduce to the min. The experiment points out that this method makes the vehicle possess good economic performance in the same driving cycle.

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