scholarly journals Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules

2020 ◽  
Vol 10 (17) ◽  
pp. 5836
Author(s):  
Jérôme Mendes ◽  
Ricardo Maia ◽  
Rui Araújo ◽  
Francisco A. A. Souza
Keyword(s):  
Fuzzy Control ◽  
Control Structure ◽  
Fuzzy Controller ◽  
Inverse Function ◽  
Time Varying ◽  
Fuzzy Logic Controllers ◽  
Control Rules ◽  
Predefined Criterion ◽  
Continuously Stirred Tank Reactor ◽  
Human Operators

The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

Application of Fuzzy Control in Transformer Cooling System

2011 ◽  
Vol 383-390 ◽  
pp. 1262-1266
Author(s):  
Zhen Guo Jia
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Cooling System ◽  
Lookup Table ◽  
Control Rules ◽  
Cooling Control ◽  
System Energy ◽  
Automation Level

Designed a device with PLC and inverter for transformer cooling control system. The structure of fuzzy controller is built with PLC. By calculating and testing, fuzzy and fuzzy control rules are established. Practical computing for the PLC to control the output lookup table is given. Using this technique, transformer cooling system energy consumption will be reduced, transformer’s coolers life will be extended and transformer cooling system automation level will be increased.

Shared Fuzzy Control of Multiple Quadrotor UAVs With Time-Dependent Delay and Bounded Control-Input Constraint

2015 ◽  
Author(s):  
Mark D. Johnson ◽  
Mohammad A. Ayoubi
Keyword(s):  
Fuzzy Control ◽  
Attitude Control ◽  
Fuzzy Controller ◽  
Equations Of Motion ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Control Input ◽  
Maximum Magnitude ◽  
Time Varying ◽  
Bounded Control

We propose a shared fuzzy controller for position and attitude control of multiple quadrotor unmanned aerial vehicles (UAVs). Using the nonlinear governing equations of motion and kinematics of a quadrotor, we develop a Takagi-Sugeno (T-S) fuzzy model for a quadrotor. Then, we consider time-varying delays due to wireless connectioninto the T-S fuzzy model. We use the sufficient stability condition based on the Lyapunov-Krasovskii stability theorem and the parallel distributed compensation (PDC) technique to determine the fuzzy control law. For practical purposes, we include actuator amplitude constraint into the design process. The problem of designing a shared fuzzy controller is cast in the form of linear matrix inequalities (LMIs). A feasible solution region is found in terms of maximum magnitude and rate of time-varying delay. In the end, the stability, performance, and robustness of the proposed shared fuzzy controller are examined via numerical simulation.

Research on Fuzzy Control Automotive Electronic Power Steering Characteristic

2013 ◽  
Vol 392 ◽  
pp. 329-332
Author(s):  
Xin Lei Ma ◽  
Yong Feng Guo
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Response Characteristic ◽  
Steering System ◽  
Vehicle Speed ◽  
Power Steering ◽  
Control Rules ◽  
Automotive Electronic ◽  
Driving Stability

The driving stability and comfort-ability are directly influenced by steering system, and it is a critical component in an automobile. Torsion bar torque sensor signal and the vehicle speed signal are used to calculate the assisting torque that on the motor shaft. The rules about how to calculate desired torque signal is very important for driving portability and stability. Influence of motor control strategy on assisting response characteristic. In this paper adaptive fuzzy control rules is proposed for the assisting characteristic and power motor. Base on assistance characteristic and assistance motor fuzzy control is designed. Fuzzy information processing torque signal, vehicle speed signal, with MATLAB simulation contrast experiment, and the result validation the fuzzy controller.

Research in the Design of the Fuzzy Controller Intelligence of the Measuring and Controlling Apparatus on Water-Content in Material Pulp

2012 ◽  
Vol 457-458 ◽  
pp. 1475-1479
Author(s):  
Guo Hong Xia
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Water Content ◽  
Fuzzy Controller ◽  
Paper Briefly ◽  
Automatic Measuring ◽  
Control Rules ◽  
Self Organized ◽  
Classical Control ◽  
Fuzzy Control Algorithm

In the course of producing material pulp, water-content (or the density of material pulp) is a very important process parameter. There are many elements which affect the water-content of material pulp, therefore it is very difficult to build precise mathematical module. In the meantime, the control system is nonlinear and retarded. For such control systems, the classical control system and modern control system is always hard to be applied, but fuzzy control can usually gain good results. This paper briefly illustrates the basic composing frame graph of the fuzzy control system of the automatic measuring and controlling apparatus on water-content in material pulp. We design the fuzzy controller of the automatic measuring and controlling apparatus on water-content in material pulp from these aspects: from the fuzzification of the sendout values, the rules of fuzzy reasoning, the adjustment of fuzzy control rules through the principle of double-factor self-organized fuzzy control, and the précising of the input values, and etc., and give the program frame graph of the fuzzy control algorithm. The successful application of the Intelligence Measuring and Controlling Apparatus on water-content proves that the design of the fuzzy controller is reasonable.

Fuzzy-Control with Gray-Predictor for Time-Varying Delay Systems

2011 ◽  
Vol 109 ◽  
pp. 306-310
Author(s):  
Zhi Yong Zhang ◽  
Xin Liu ◽  
Cai Xia Huang
Keyword(s):  
Fuzzy Control ◽  
Control Structure ◽  
Delay Systems ◽  
Step Response ◽  
Time Varying ◽  
Time Varying Delay ◽  
Gray Prediction ◽  
Control Scheme ◽  
Varying Delay ◽  
Fast Dynamic

A control structure, integrated with the virtue of high approximate capability of neural network, strong adaptability of gray prediction and strong robustness of fuzzy control, is proposed in this paper, in which the time-delay identify, varying step response predict and fuzzy control are achieved at one time. The results of computer simulation indicate that the control scheme can dramatically satisfy the desire of fast dynamic response and stability．

Identification of Fuzzy Rule on Manual Control of an Unstable System

1995 ◽  
Vol 7 (2) ◽  
pp. 100-107
Author(s):  
Shigehiro Masui ◽  
◽  
Toshiro Terano ◽  
Yoshimasa Sugaya ◽  
◽  
...  
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Human Operator ◽  
State Variables ◽  
Initial Deviation ◽  
Control Rules ◽  
Linguistic Rules ◽  
Skilled Operator ◽  
Human Operators

After some training, human operators can manually control very unstable objects when some proper information is given. But they can hardly explain how they do it, because they operate them intuitively and not logically. In this paper, we study the human behavior during the control of a double inverted pendulum and identify its control rules experimentally. The motion of a double inverted pendulum is simulated by a micro-computer and some of the state variables are indicated on a CRT, observed by a subject, and controlled on a keyboard. In order to find which information is used by a subject, his visual points are examined by an eye-camera. As a result, we see that there are three phases of operation, that is, the decrease of initial deviation, the prevention of over-shoot, and the keeping of stability. Next, the motion of a pendulum is analyzed qualitatively in each phase so as to identify the control rules of a human operator. By this analysis, we see that the intuitive manipulator of the human operator is quite reasonable from the physical viewpoint, and we represent it by some linguistic rules. From these results, we suggest a hierarchical structure of fuzzy rules as a model of a human operator which is verified through experiments on fuzzy control. It is concluded that this fuzzy controller acts as a skilled operator, but its performance is far superior to humans.

Fuzzy Control with Adjustable Factors in Tension Control System

2014 ◽  
Vol 902 ◽  
pp. 201-206 ◽  
Author(s):  
Fang He ◽  
Jia Han ◽  
Qiang Wang
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Tension Control ◽  
System Response ◽  
Fast System ◽  
Fuzzy Control System ◽  
Control Rules ◽  
Tension Control System ◽  
Fuzzy Control Algorithm

The variable tension control system of strip winding is a nonlinear, strong coupling and time-varying system. Traditional fuzzy controller with fixed control rules cannot obtain the desired control performance of the strip winding system. So the fuzzy control algorithm with adjustable factorαis proposed, and the introduction of adjustable factors can change the fuzzy control rules. The tension fuzzy controller with adjustable factorαis designed, and simulation model of the system is established using Matlab software. The result of simulation shows that tension fluctuation of the tension fuzzy control system with adjustable factor get small, comparing the tension fuzzy control system with adjustable factors with the ordinary tension fuzzy control system. The tension fuzzy control system with adjustable factors has fast system response and strong anti-interference ability.

A study of the use of fuzzy control theory to stabilize the gait of biped robots

Robotica ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 777-790 ◽  
Author(s):  
Hai-Wu Lee
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Linear Quadratic Regulator ◽  
Biped Robot ◽  
Linear Quadratic ◽  
Biped Robots ◽  
Control Rules ◽  
Lqr Controller ◽  
Stable Gait ◽  
Differential Control

SUMMARYThis paper designs a biped robot to perform appropriate walking exercises according to the terrain, which then walks stably on a flat environment. The concept of fuzzy logic is combined with the Linear Quadratic Regulator (LQR) controller theory to design the best method to allow the biped robot system to have a balanced and stable gait. Traditional controllers are designed using mathematical models of physical systems, but a fuzzy controller is a physical system that uses an inexact mathematical model, which involves sets and membership functions. Fuzzy controllers use fuzzification, fuzzy control rules, and defuzzification. The method and theory of control: A stable gait for robots is achieved using inverse kinematics, fuzzy concepts, the LQR controller theory, path design, and the characteristics of a dynamic equation. It is then simulated using mathematical tools to prove that the system eliminates swinging by biped robots without fuzzy control knowing beforehand the dynamic model the system is using. Proportional-Integral-Differential control achieves a stable gait design in a flat environment.

Fuzzy Control Using Piecewise Linear Membership Functions Based on Knowledge of Tuning a PID Controller

2001 ◽  
Vol 5 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Kenichiro Hayashi ◽  
◽  
Akifumi Otsubo ◽  
Kazuhiko Shiranita
Keyword(s):  
Fuzzy Control ◽  
Pid Controller ◽  
High Speed ◽  
Fuzzy Controller ◽  
Piecewise Linear ◽  
Membership Functions ◽  
Trial And Error ◽  
Tuning Parameters ◽  
Control Rules ◽  
Set Up

Tuning control parameters of a fuzzy controller depends on trial-and-error. How this can be accomplished efficiently is an important subject in fuzzy control that should be investigated. We propose a method in which membership functions of a fuzzy controller can be simply set up using the knowledge of tuning parameters in a conventional PID controller. In this method, fuzzy control is realized as follows: Piecewise linear membership functions determined using the knowledge of tuning parameters in a PID controller are set up in antecedent parts of four fuzzy control rules having simple structures. Then the simplified inference method that enables high-speed inference is applied to fuzzy control rules.

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