Aytekin Bagis

Determining fuzzy membership functions with tabu search—an application to control

Abstract This paper presents a new approach for the optimum determination of membership functions for a fuzzy logic controller based on the use of tabu search algorithm. To demonstrate the efficiency of the suggested approach, a specific control problem—operation of spillway gates of reservoirs during floods is selected. Simulation results showed that the proposed approach could be employed as a simple and effective optimization method for achieving optimum determination of membership functions.

Controlling spillway gates of dams by using fuzzy logic controller with optimum rule number

Reservoir operation of dams during floods is a complex, nonlinear, nonstationary control process and is significantly affected by hydrological conditions which are not predictable beforehand. In this paper, an operation method based on fuzzy logic control is presented for the operation of spillway gates of reservoirs during floods. The rule base of fuzzy logic controller is optimally determined by using tabu search algorithm which is a modern popular heuristic algorithm. Simulation results demonstrate that the proposed approach based on fuzzy logic controller designed by using tabu search produces an accurate and efficient solution for the reservoir operation of dams.

Tabu search algorithm based PID controller tuning for desired system specifications

Abstract This paper presents a tuning approach based on a tabu search algorithm (TSA) to obtain the optimal proportional-integral-derivative (PID) controller parameters in order to achieve a desired transient response. TSA is used to determine the main parameters of the PID controller. The performance of the PID controlled system is examined by considering the characteristics of the step response of the plant. Simulation results demonstrate that the tabu algorithm based approach is one of the useful methods for PID controller tuning, and using by the presented method, performance of the controlled system can be significantly improved according to the given control specifications.

Evolutionary algorithm-based fuzzy PD control of spillway gates of dams

In this paper, an evolutionary algorithm (EA)-based fuzzy proportional-derivative (PD)-type controller is employed to reservoir control of dams with the purpose of operating spillway gates during any flood of any magnitude, which is not predictable beforehand. EA is used to evolve the main parameters of the fuzzy PD controller. The use of the EA, in conjunction with a systematic neighborhood structure for the determining of fuzzy rule-base parameters, leads to a significant improvement in the performance of the controller. The major objective of the controller is to achieve better system performance over the conventional control methods. In order to demonstrate the high performance of the presented method, we simulate the control system using different probable inflow hydrographs of various magnitudes. The simulation results indicate that the EA-based fuzzy PD controller not only performs an accurate and efficient solution, but also exhibits more desirable and reliable results than the conventional approaches.

Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling

This paper is an extended version of the paper presented at TOK 2014 (Turkish Automatic Control National Meeting) which examined the determination of Sugeno type fuzzy model parameters optimized by the artificial bee colony (ABC) algorithm for a microstrip antenna. This paper presents a performance comparison of the Sugeno and Mamdani type fuzzy models proposed for nonlinear system modelling. To determine the parameters of the fuzzy models, the ABC algorithm is used. For this purpose, several nonlinear system examples which given in the literature were considered, and the results obtained by the optimized fuzzy models were compared with the other modelling approaches in the literature. Simulation results demonstrate that the use of the ABC algorithm provides a remarkable contribution to the model’s performance.

An efficient control method for elevator group control system

This paper presents an efficient control approach for the elevator group control system. The essential of the method used is based on an operation strategy with a talented algorithm. In order to analyze the performance of the presented system, the control method was evaluated by considering different performance characteristics in the elevator group control system. The results of the presented method compared with the results of the area weight algorithm.

Performance comparison of genetic and tabu search algorithms for system identification

This paper presents a performance comparison of the genetic and tabu search algorithm for system identification operations of different processes. The identification procedure is based on open-loop step response analysis of the processes. Each of the two algorithms is applied to determine the optimal parameter values of the processes to be modelled. Simulation results demonstrated that the presented algorithms can be efficiently used in the identification problems.

Fractional PID controller design for fractional order systems using ABC algorithm

This paper provides a discussion on the performance of the fractional order proportional-integral-derivative (FOPID)controllers designed by using artificial bee colony (ABC) algorithm for fractional order systems. Some measures of the step response, i.e., integral of time weighted absolute error (ITAE), integrated squared error (ISE), settling time, and overshoot, are used to evaluate the performance of the FOPID controlled systems. Simulation experiments are compared with the success of the classical PID controllers optimized by the ABC algorithm, and with the other methods given in literature. The results demonstrate that ABC algorithm based FOPID controlled systems perform significantly better performance than the other ones.

Elevator group control by using talented algorithm

A simple and efficient control approach based on an operation strategy with a talented algorithm is presented for the elevator group control. In order to analyze the performance of the presented system, the control method was evaluated by considering different performance characteristics. The results of the method were compared with the results of the area weight algorithm. The results obtained from the simulations indicated that the presented approach exhibits high performance over the area weight algorithm with the minimum time consumption results.

Simultaneous computation of the speed and fuel parameters of flight control system by using Anfis and artificial neural networks

As a natural result of the growing air transportation, the importance of the fligth control systems that simultaneously evaluates the many parameters has increased greatly. This study presents the results of a modeling examination based on the use of Anfis and artificial neural networks for simultaneously determination of speed and fuel parameters of the flight control system. In the study given for Boeing B-767-200 type planes, model structures have three inputs y, a, r which are altitude, weigth, and engine pressure ratio, and two outputs h, f which specifies flying speed and fuel amount, respectively. The results obtained from the models are presented in comparison with the actual values.