Miroslav Pokorný

Conventional controller design based on Takagi-Sugeno fuzzy models

Although it is long since the first PID controller design method was developed, new methods are still being created and no general applicable method has been found. The paper extends the set of designing methods of PID controllers. Design methods can be divided into classic (analytical tuning methods, optimization methods etc.) and not so common fuzzy knowledge based methods. In this case, a conjunction of both methods is presented. The classic design methods (Ziegler-Nichols step response method and Chien, Hrones and Reswick design method) are used for achieving the knowledge of fuzzy knowledge based design methods. The fuzzy knowledge based design methods are based on Takagi-Sugeno model. It is defined as a class of systems for which - using the new proposed method - the settling time is shorter or the settling time is nearly the same but without overshoot, which could be also very useful. The proof of efficiency of the proposed methods and a numerical experiment is presented including a comparison with the conventional methods (simulated in the software environment Matlab-Simulink).

Fuzzy Linear Regression Analysis

Abstract The theoretical background for abstract formalization of vague phenomenon of the complex systems is fuzzy set theory. In the paper vague data as specialized fuzzy sets - fuzzy numbers are defined and it is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. Interval and fuzzy regression technologies are discussed, the linear fuzzy regression model is proposed. To identify fuzzy regression coefficients of model genetic algorithm is applied. The numerical example is presented and the possibility area of vague model is illustrated.

Takagi-Sugeno Fuzzy Model in Task of Controllers Design

The designing of PID controllers is in many cases a discussed problem. Many of the design methods have been developed, classic (analytical tuning methods, optimization methods etc.) or not so common fuzzy knowledge based methods. In this case, the amount of fuzzy knowledge based methods is extended. New way of designing PID controller parameters is created, which is based on the relations of Chien, Hrones and Reswick design method (the modification of Ziegler-Nichols step response method). The proof of efficiency of the proposed method and a numerical experiment is presented including a comparison with the conventional Chien, Hrones and Reswick method (simulated in the software environment Matlab-Simulink). It is defined a class of systems for which - using the new proposed method - the settling time is shorter or the settling time is nearly the same but without overshoot, which could be also very useful.

Microprocessor Based Mobile Robot Control System

Abstract The model of mobile robot is designed and performed using the three-wheels principle. Each of wheels is derived through a separate electrical motor-drive. The actual motion trajectory is defined by the actual turning frequency of motors. The motor-drivers are controlled using the microprocessor unit calculating the motion control signals for each of one. Global driving signal is calculated and transmitted through the HF radio communication equipment using the central PC self-learning control fuzzy oriented system. Distributed robot’s control system is performed using the MOTOROLA M68HC11 family equipment.

System Identification Using Genetic Algorithms

System identification is one of the necessary tasks in controller design and its adaptation. Many identification methods are known, and new ones are still being developed in order to find a better solution for huge scale of cases. In the paper identification of system of 2nd order systems using genetic algorithms is demonstrated. In presented case genetic algorithms are used for finding parameters of difference equation of the controlled system and it substitutes classic, conventional optimization methods. Proposed method can be used for continuous identification or it can be activated in defined time points on stored data. And on the other hand, presented task is also a case of a specific usage of genetic algorithms and it can serve as a proof of efficiency of this non-conventional optimization method (simulated in the Matlab&Simulink software environment).

Double Expert System for Monitoring and Re-adaptation of PID Controllers

Finding of monitoring systems for deciding if or how re-adapt a PID controller in literature is not so complicated. These monitoring systems are also widely used in industry. But monitoring system which is based on non-conventional methods for deciding, takes into account the non-numeric terms and it is open for adding more rules, is not so common. Presented monitoring is designed for systems of second order and it is performed by the fuzzy expert system of Mamdani type with two inputs - settling time compared with the previous settling time (relative settling time) and overshoot. It is supplemented by using of non-conventional method for designing of classic PID controller. So it can be called as double expert system for monitoring and following re-adaptation of classical PID controller. The proof of efficiency of the proposed method and a numerical experiment is presented by the simulation in the software environment Matlab-Simulink.

A20: Using a PLC Tecomat TC600 as a fuzzy regulator in the problem of an inverted pendulum system regulation

Abstract This work solves a problem of the inverted pendulum system regulation using a fuzzy regulator. As this regulator has been used the programmable logic controller (PLC) Tecomat TC600, type 607. The PLC is connected to the personal computer (PC) by the use of a voltage converter and an industrial PC card PCI 1751. The PC works as a simulation device of the inverted pendulum system, where the simulation is performed by a program created in the Delphi programming language version 5. The fuzzy model used in the fuzzy regulator is designed directly for the inverted pendulum system. The whole application has been successfully tested and also very good results have been reached.