TS Control — The Link between Fuzzy Control and Classical Control Theory

2005 ◽  
pp. 181-194 ◽  
Author(s):  
Kai Michels
Keyword(s):  
Fuzzy Control ◽  
Control Theory ◽  
Classical Control

Error Analysis of Mechanism with Clearances and Fuzzy Control

2011 ◽  
Vol 128-129 ◽  
pp. 855-858
Author(s):  
Kun Rong Huang
Keyword(s):  
Fuzzy Control ◽  
Control Theory ◽  
Intelligent Control ◽  
Self Regulation ◽  
Control Effect ◽  
Modern Control Theory ◽  
Intelligent Control System ◽  
Nonlinear Error ◽  
The Stability ◽  
Classical Control

The mechanism with clearance is a nonlinear system, then using classical control theory and modern control theory cannot obtain satisfactory control effect. Therefore，In order to control the nonlinear error cause by clearance, the conventional fuzzy control and parameter self-regulation fuzzy control are considered by using the fuzzy control’s humanization and intelligent. The results of test show that: the nonlinear error got smaller and the stability of system got obvious improvement as the introduction of intelligent control system.

Fuzzy control based on genetic algorithm in intelligent psychology teaching system

2021 ◽  
pp. 1-9
Author(s):  
Xiaojia Pang ◽  
Yuwen Ning
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Control Theory ◽  
Classroom Teaching ◽  
Improved Genetic Algorithm ◽  
Group Function ◽  
Teaching Process ◽  
Teaching Management ◽  
Teaching System ◽  
Volume Group

The advancement of science has made computer technology and the education industry more and more closely related, and the development of intelligent teaching systems has also opened a new path for classroom teaching. This paper studies the application of fuzzy control based on genetic algorithms in the intelligent psychology teaching system. Facing the complicated variables in the teaching process, the improved genetic algorithm can better realize dynamic teaching decisions through fuzzy control. This article aims to improve the quality of psychology classroom teaching, and develops an intelligent psychology teaching system based on the fuzzy control theory of genetic algorithm. Combined with the current development of fuzzy control theory, the problems existing in the intelligent teaching system are studied and analyzed, and they have been optimized and improved. This paper proposes a control algorithm based on a teaching management system. The algorithm can implement fuzzy control on student models, knowledge organization structure, intelligent test papers and teaching decision-making. While restoring the real teaching process, it can better realize teaching students in accordance with their aptitude and improve teaching. The intelligence of the system. According to the system test data, the proportions of the difficulty of the system’s automatic test paper are 30.1%, 51.6%, 18.3%, which are in line with the designer’s set expectation of 3 : 5:2, which shows the improved genetic algorithm. It can realize the intelligent volume group function very well.

Control Theory and Decision Making in Organisations A Reconnaissance

1970 ◽  
Vol 3 (3) ◽  
pp. T46-T48 ◽  
Author(s):  
G. L. Mallen
Keyword(s):  
Control Theory ◽  
Control Systems ◽  
Decision Process ◽  
Transfer Functions ◽  
Social Systems ◽  
Simulation Methods ◽  
Industrial Dynamics ◽  
Simulation Approach ◽  
Decision Systems ◽  
Classical Control

Differences between the domains of application of classical control theory and applied cybernetics are examined. It is suggested that a unifying concept for the understanding of both simple mechanical control systems and complex social systems is that of the decision process. Simple decision systems are equated to those for which transfer functions can be specified. Complex systems demand a simulation approach. No prescriptive organisational control theory based on simulation methods yet exists but one is required and is seen to be emerging from such diverse fields as artificial intelligence and Industrial Dynamics.

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.

Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum

2013 ◽  
Vol 765-767 ◽  
pp. 2004-2007
Author(s):  
Su Ying Zhang ◽  
Ying Wang ◽  
Jie Liu ◽  
Xiao Xue Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Control Theory ◽  
Inverted Pendulum ◽  
Learning Ability ◽  
Adaptive Neural Network ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Self Learning ◽  
Controlled Object

Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functions of neural network algorithm. We called it adaptive neural network fuzzy inference system (ANFIS). ANFIS not only takes advantage of the fuzzy control theory of abstract ability, the nonlinear processing ability, but also makes use of the autonomous learning ability of neural network, the arbitrary function approximation ability. The controller was applied to double inverted pendulum system and the simulation results showed that this method can effectively control the double inverted pendulum system.

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