Application of Fuzzy Control to a Riderless Bicycle

2000 ◽  
Vol 4 (3) ◽  
pp. 195-199
Author(s):  
Gérard Lachiver ◽  
◽  
Saïd Berriah
Keyword(s):  
Remote Control ◽  
Fuzzy Control ◽  
Automatic Control ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Scale Model ◽  
Control Architecture ◽  
Stabilization System ◽  
Computer Engineering ◽  
Proportional Integral

A scale model of a bicycle equipped with a stabilization system was developed in the Mechatronics laboratory of the Department of Electrical Engineering and Computer Engineering of the Universite de Sherbrooke. Firstly, a proportional integral automatic control with adjusted gain was developed to make possible the riding of a bicycle using a remote control. Secondly, an intelligent control architecture based on a fuzzy controller was developed to make the bicycle stable and duplicate a human rider.

Intelligent Control of a Cart and Pole System

2013 ◽  
Vol 397-400 ◽  
pp. 1438-1441
Author(s):  
Hong Li Jia ◽  
Qiang Liu ◽  
Feng Du
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Real Life ◽  
Fine Tuning ◽  
Vehicle Simulation ◽  
Engineering Software ◽  
Wide Range ◽  
Pole System

The paper focuses on the cart-pole system by using fuzzy control, fuzzy mamdan control theory, the fuzzy controller perturbation amplitude simulation and fine-tuning of different linear and nonlinear model. It applicates MATLAB soft which function is so powerful, suitable for a wide range of engineering software system for vehicle simulation intelligent control. And then adjusting fuzzy controller on PID control system, fuzzy control system, the cart-pole system can provide reliable data for our real life and production in the future.

scholarly journals Modeling of automatic control system for air supply to a bio-reactor using fuzzy control

2021 ◽  
Vol 2131 (2) ◽  
pp. 022009
Author(s):  
V F Lubentsov ◽  
E A Shakhrai ◽  
E V Lubentsova
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Fuzzy Control ◽  
Automatic Control ◽  
Automatic Control System ◽  
Fuzzy Controller ◽  
Nonlinear Controller ◽  
Steady State Mode ◽  
Air Supply

Abstract The stages of modeling the automatic control system (ACS) for air supply to aeration with the use of fuzzy control are considered. The investigated control algorithm is based on the combination of a nonlinear controller with approximating control (CAC), whose parameters are corrected using fuzzy logic. The algorithm for correcting the CAC parameters for transient and steady state modes is based on the application of two simple rulebases (RB) with three and five linguistic terms, respectively. As a result, the required speed in the transient mode and accuracy in the steady state mode are provided. It is proved that switching the RB according to the logic of the multi-mode system is less demanding on the number of rules, structure and setting parameters of the membership function than using the extended RB. The differences between the proposed ACS with different BP for the main operating modes of the system are shown. These include: improvement of quality indicators due to the implementation of different BP in different modes; more rigorous justification of the mechanism for ensuring insensitivity to the switching moments of BP when changing modes due to the CAC of the direct circuit of the ACS. Effective implementation of the stages of ACS modeling and fuzzy controller design is possible using the Fuzzy Logic Toolbox system of the Simulink MATLAB modeling environment.

Self-Regulation PID-Fuzzy Controller for Flume System

2010 ◽  
Vol 26-28 ◽  
pp. 462-465
Author(s):  
Xi Zhi Zhu
Keyword(s):  
Process Control ◽  
Fuzzy Control ◽  
Production Process ◽  
Fuzzy Controller ◽  
Self Regulation ◽  
Time Change ◽  
Extreme Condition ◽  
Fuzzy Controllers ◽  
Proportional Integral

Since complex industry object has characteristic of non-linearity, uncertainty and time-change, this paper studied about application of self-regulation PID-Fuzzy controller for flume system. The properties of the basic fuzzy controllers can be improved through compiling S-function and subsystem that automatically corrects the quantizing factors Ke and Kc. The proportional, integral and derivative constant adjusted by new rule of fuzzy to adapt with the extreme condition of process. Fuzzy control applies language rule to describe controlling process and bases rule to modify the controlling arithmetic and parameter. The new algorithm performs in every condition and already tested in every extreme condition. The simulation result shows that the controller possesses virtues of self-adaptability and short adjusting time. It will realize production process control.

Different Fuzzy Controllers Analysis for MR Structures

2012 ◽  
Vol 204-208 ◽  
pp. 2874-2877
Author(s):  
Ying Qing Guo
Keyword(s):  
Fuzzy Control ◽  
Intelligent Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Fuzzy Controllers ◽  
Mr Dampers ◽  
Fuzzy Control Strategy ◽  
Simulated Analysis

The fuzzy control technology is a kind of intelligent control method, and it has strong robustness. The fuzzy control strategy is used to choose the control currents of MR dampers in this paper. In order to illustrate how to design the fuzzy controller, three kinds of the fuzzy controller having different membership functions and fuzzy rules are designed. A five-floor MR structure using the designed different fuzzy controllers is simulated. Analysis results show that for the MR structure, it is not best that MR dampers provide the maximum forces to the structure, so the displacement and acceleration responses of the structure must be weighed at the same time to make MR dampers provide reasonable forces when the membership functions and fuzzy rules are designed.

Research on Anti-Swing Control of Bridge Crane Based on Fuzzy Control

2011 ◽  
Vol 143-144 ◽  
pp. 293-296 ◽  
Author(s):  
Zhi Hui Zeng ◽  
Liang Li
Keyword(s):  
Fuzzy Control ◽  
Control Theory ◽  
Intelligent Control ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Response Speed ◽  
Bridge Crane ◽  
Nonlinear Mathematical Model ◽  
Simulink Simulation ◽  
Control Accuracy

In recent years, with the development of intelligent control theory, fuzzy control theory has been applied to automatic control of the crane. This paper designs fuzzy anti-swing controller based on the nonlinear mathematical model of bridge crane, and MATLAB/Simulink simulation was carried out. The Simulink experiment proves that location fuzzy controller and angle fuzzy controller show the good anti-swing effect compared with conventional PID controller. The two controllers not only improve response speed times but also improve control accuracy.

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

Research on Fuzzy Control of Mine Ventilation Based on Embedded Systems

2014 ◽  
Vol 686 ◽  
pp. 126-131
Author(s):  
Xiao Yan Sha
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Fuzzy Controller ◽  
Simulation Analysis ◽  
Control Unit ◽  
Real Time Control ◽  
Embedded Processor ◽  
Feed Forward ◽  
Time Control ◽  
Software And Hardware

Taking embedded processor as the core control unit, the paper designs the fan monitoring system software and hardware to achieve the fan working condition detection and real-time control. For the control algorithm, the paper analyzes the fuzzy control system theory and composition, and then combined with tunnel ventilation particularity, introduce feed-forward model to predict the incremental acquisition of pollutants to reduce lag, combined with the system feedback value and the set value, by calculate of two independent computing fuzzy controller, and ultimately determine the number of units increase or decrease in the tunnel jet fans start and stop. Through simulation analysis, the introduction of a feed-forward signal, it can more effectively improve the capability of the system impact of interference.

Self-Adaptive Laser Power Stabilization System Based on Fuzzy Control

2021 ◽  
Vol 48 (1) ◽  
pp. 0101003
Author(s):  
欧阳鑫川 Ouyang Xinchuan ◽  
杨博文 Yang Bowen ◽  
万金银 Wan Jinyin ◽  
肖玲 Xiao Ling ◽  
成华东 Cheng Huadong
Keyword(s):  
Fuzzy Control ◽  
Laser Power ◽  
Stabilization System ◽  
Power Stabilization ◽  
Adaptive Laser ◽  
Self Adaptive

Analysis and Synthesis of a Fuzzy Controller by the Phase Plane Method

2021 ◽  
Vol 22 (10) ◽  
pp. 507-517
Author(s):  
Y. A. Bykovtsev
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Automatic Control System ◽  
Phase Plane ◽  
Fuzzy Controller ◽  
Piecewise Linear ◽  
Plane Method ◽  
Phase Trajectories ◽  
Analysis And Synthesis ◽  
Phase Plane Method

The article is devoted to solving the problem of analysis and synthesis of a control system with a fuzzy controller by the phase plane method. The nonlinear transformation, built according to the Sugeno fuzzy model, is approximated by a piecewise linear characteristic consisting of three sections: two piecewise linear and one piecewise constant. This approach allows us to restrict ourselves to three sheets of phase trajectories, each of which is constructed on the basis of a second-order differential equation. Taking this feature into account, the technique of "stitching" of three sheets of phase trajectories is considered and an analytical base is obtained that allows one to determine the conditions for "stitching" of phase trajectories for various variants of piecewise-linear approximation of the characteristics of a fuzzy controller. In view of the specificity of the approximated model of the fuzzy controller used, useful analytical relations are given, with the help of which it is possible to calculate the time of motion of the representing point for each section with the involvement of the numerical optimization apparatus. For a variant of the approximation of three sections, a technique for synthesizing a fuzzy controller is proposed, according to which the range of parameters and the range of input signals are determined, at which an aperiodic process and a given control time are provided. On the model of the automatic control system of the drive level of the mechatronic module, it is shown that the study of a fuzzy system by such an approximated characteristic of a fuzzy controller gives quite reliable results. The conducted studies of the influence of the degree of approximation on the quality of control show that the approximated characteristic of a fuzzy controller gives a slight deterioration in quality in comparison with the smooth characteristic of a fuzzy controller. Since the capabilities of the phase plane method are limited to the 2nd order of the linear part of the automatic control system, the influence of the third order on the dynamics of the system is considered using the example of a mechatronic module drive. It is shown that taking into account the electric time constant leads to overshoot within 5-10 %. Such overshoot can be eliminated due to the proposed recommendations for correcting the static characteristic of the fuzzy controller.

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