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Tuning PID controller using genetic algorithms for electro-hydraulic system with tracking force control
(2014-01-01)
Moonumca, Pisan
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Depaiwa, Nattawoot
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Yamamoto, Yoshio
The aim of this paper is design a controller for force control of an Electro-Hydraulic System and selection of a PID gains are using genetic algorithm. The mathematical model is considered as the Newton second law and compressible fluid flow theory. This paper compares two kinds of tuning methods for PID controller. One is the controller design by the genetic algorithm, second is the controller design by the Ziegler and Nichols method. Each PID gains are tested with step input function, square signal, half-sine signal and half-saw signal. And results show performance index with rise time, settling time and Maximum %OS. It was found that the proposed PID gains tuning by the genetic algorithm is better than the Ziegler & Nichols' method. © (2014) Trans Tech Publications, Switzerland.
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Simplified genetic algorithm: Simplify and improve RGA for parameter optimizations
(2014-01-01)
Ngamtawee, Ruksaphol
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Wardkein, Paramote
The structural complexity and complicated generic operators of Genetic Algorithm (GA) contribute to its slow computational speed. Furthermore, GA and other similar algorithms with a small population size are vulnerable to the problem of premature convergence. Premature convergence causes the algorithms to stagnate and stop searching, giving rise to wasteful computation. Even though the problem can be addressed with a larger population size, computational time is inevitably increased. This research paper has thus proposed Simplified Genetic Algorithm (SimpGA). This algorithm utilizes a one-pair-builtall structure in which only two parent chromosomes are required to produce the entire population (offspring). Rather than relying on the conventional operators, simplified operators, i.e. timer mutation, diform crossover and topmost selection, are used in the proposed SimpGA. In addition, tests are carried out with SimpGA on four test functions and four applications. The experimental results show that SimpGA is simpler to implement and performs well, especially in a small population environment.
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Estimation of algae growth model parameters by a double layer genetic algorithm
(2012-11-01)
Nokkaew, Artorn
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Pimpunchat, Busayamas
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Modchang, Charin
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Amornsamankul, Somkid
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Triampo, Wannapong
This paper presents a double layer genetic algorithm (DLGA) to improve performance of the information-constrained parameter estimations. When a simple genetic algorithm (SGA) fails, a DLGA is applied to the optimization problem in which the initial condition is missing. In this study, a DLGA is specifically designed. The two layers of the SGA serve different purposes. The two optimizations are applied separately but sequentially. The first layer determines the average value of a state variable as its derivative is zero. The knowledge from the first layer is utilized to guide search in the second layer. The second layer uses the obtained average to optimize model parameters. To construct a fitness function for the second layer, the relative derivative function of the average is combined into the fitness function of the ordinary least square problem as a value control. The result shows that the DLGA has better performance. When missing an initial condition, the DLGA provides more consistent numerical values for model parameters. Also, simulation produced by DLGA is more reasonable values than those produced by the SGA.
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Static output feedback robust loop shaping control for grid connected inverter using genetic algorithms
(2012-09-01)
Nimpitiwan, Natthaphob
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Kaitwanidvilai, Somyot
At present, robust analysis is an important issue for designing controllers in inverter-based distributed generation systems. In these systems, the fluctuation of DC voltage level from renewable resources (e.g., fuel cells, solar cells, wind turbines) and loading conditions cause system uncertainties and disturbances. To enhance the robust performance of the DC systems, a new class of inverter controller is required to achieve both robustness and performance of entire systems. In this paper, the proposed technique applies Genetic Algorithms (GA) to determine the optimal controller parameters so that stability margin of the controlled system is maximized. In addition, the structure of (Proportional-Integral-Derivative) PID with first order derivation filter is selected as the final controller to realize a practical robust controller. As results indicated, stability margin of the proposed controller is better than that of the reduced order controller designed by Hankel Norm model reduction. The proposed technique, hence, offers a robust controller with a guaranteed stability margin; also it is practically feasible to be implemented. © 2012 ICIC International.
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Robust control design for three-phase power inverters using genetic algorithm
(2012-01-01)
Nimpitiwan, Natthaphob
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Kaitwanidvilai, Somyot
This paper proposes a new technique to design a fixed-structure robust controller for grid connected three-phase inverter systems. The proposed technique applies the Genetic Algorithm to evaluate the optimal controller parameters. The integral squared error (ISE) of the controlled system is minimized and the robust performance (RP) of the system is satisfied. In the proposed design, the structure of controller is specified as a decentralized Proportional-Integral (PI) controller which is preferred for practical implementations. Simulation results show that the proposed technique is promising. Applying the proposed technique ensures wide operating conditions for three-phase power inverters.
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Development of new edge-detection filter based on genetic algorithm: An application to a soldering joint inspection
(2010-02-01)
Saenthon, A.
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Kaitwanidvilai, S.
This paper proposes a new technique of edge detection for inspecting edge and perfection of soldering joint in the flip-chip, which is an important part of a hard disk drive. Summation of error between the actual values and the measured values from the designed system of several data sets is formulated as the objective function. Genetic algorithm is adopted to find the optimal filter mask to enhance the accuracy of the inspection system. As the results indicated, the accuracy of a system with the proposed edge-detection technique is superior to that of a system with conventional filters. © 2009 Springer-Verlag London Limited.
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Prototype selection based on minimal consistent subset and genetic algorithms
(2008-12-01)
Kruatrachue, Boontee
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Hongsamart, Marut
This paper applies the genetic algorithms to identify the minimal "consistent" prototype subset [1]. This subset can be used as a prototype which correctly recognizes the entire original prototype set. This proposed genetic algorithm tries to And the minimal consistent subset to reduce recognition time in nearest neighbor [2] classification. The main difference from other genetic algorithm (GA) approaches is the hybrid of minimal consistent set identification (MCSI) method [3] and genetic algorithm. The MCSI method provides the local optimal number of prototype while the Genetic performs the global search. The proposed hybrid algorithm has been tested on several problems and compared with the results of MCSI and other GA approach [4]. © 2008 SICE.
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A new edge detection technique for an automatic visual inspection system using genetic algorithms
(2008-01-01)
Saenthon, A.
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Kaitwanidvilai, S.
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Kunakorn, A.
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Ngamroo, I.
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Kanprachar, S.
This paper proposes a new technique of edge detection for inspecting edge and perfection of soldering joint in the pre-amplifier circuit, which is an important part in hard disk drive. Summation of error between the actual value and the measured value from the designed system of several data sets is formulated as the objective function. Genetic Algorithm (GA) is adopted to find the optimal filter mask which enhances the accuracy of the inspection system. As results indicated, accuracy of the system with the proposed edge detection technique is superior to that of the system with conventional filters. In addition, inspection time of the developed system is much faster than that of human inspection. © 2008 IEEE.
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Minimum cost design of small low-loss transformers
(2005-01-01)
Phophongviwat, T.
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Chat-uthai, C.
A procedure for minimum cost design of a small low-loss low-frequency transformer is presented. The total losses of transformer can be defined to be the design constraint. The procedure for improving the accuracy of calculated total losses is the basis of the proposed total losses calculation associated with the temperature rise evaluation. The optimization technique used in this paper is based on the Genetic Algorithms. Five samples of 72 VA, 220/24 V, 50 Hz transformers have been designed and tested in order to illustrate how well the calculated total losses values using this proposed procedure matches that of the measured values obtained from the test. Finally, the simulation results of the minimum cost design with losses constraint are demonstrated. This proposed design is suitable for the design of economical transformer with total losses value options which is considerably flexible for a particular purpose.
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Estimation of induction motor parameters based on field test coupled with genetic algorithm
(2002-01-01)
Phumiphak, T.
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Chat-Uthai, C.
This paper proposed a technique for estimating the parameters of three-phase induction motor in order to conduct on-site energy audits of existing motors, which are then used to project a cost savings. This proposed technique uses only a few sets of data (voltage, current, speed, power factor or torque if possible) from the field test of motor (on-site), instead of the no-load and blocked rotor tests, coupled with the genetic algorithm for evaluating the equivalent circuit parameters. Once these parameters are known it is possible to obtain the operating performances (50-100%) of the motor such as efficiency, current, torque. This technique could be suitable for the general purpose drive applications when the motor cannot operate at no-load since its shaft is permanently connected to its load. To illustrate how well the performances of the estimated model matches that of the actual motor obtained from load test, the results of 3 HP and 5 HP induction motors will be presented and compared.

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