• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.686-689
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• 2004

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.

• Journal of Power System Engineering
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• v.17 no.5
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• pp.86-93
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• 2013

This paper proposes the used of genetic algorithm for optimizing PI controller and describes the dynamic modeling simulation for the permanent magnet synchronous motor driven by simplified vector control with the aid of MATLAB-Simulink environment. Furthermore, three kinds of error criterion minimization, integral absolute error, integral square error, and integral time absolute error, are used as objective function in the genetic algorithm. The modeling procedures and simulation results are described and presented in this paper. Computer simulation results indicate that the genetic algorithm was able to optimize the PI controller and gives good control performance of the system. Moreover, simplified vector control on permanent magnet synchronous motor does not need to regulate the direct axis component current. This makes simplified vector control of the permanent magnet synchronous motor very useful for some special applications that need simple control structure and low cost performance.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2763-2765
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• 2003

Generally, we made use of PID control for torque control, speed control and stability, Hence, dynamic characteristic of DC motor has been studied for stable drive and accurate speed control by many engineers. But, in this paper, we applied genetic algorithm to current control for robust control and stability In conclusion, we prove that current control of genetic algorithm can be high efficiency.

• Journal of Mechanical Science and Technology
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• v.15 no.5
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• pp.580-591
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• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.610-616
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• 2000

This paper presents an active noise control algorithm via Walsh transform domain controller learned by genetic algorithm. Typical active noise control algorithms such as the filtered-x lms algorithm are based on the gradient algorithm. Gradient algorithm have two major problems; local minima and eigenvalue ratio. To solve these problems, we propose a combined algorithm which consist of genetic learning algorithm and discrete Walsh transform called Walsh Transform Domain Genetic Algorithm(WTDGA). Analyses and computer simulations on the effect of Walsh transform to the genetic algorithm are performed. The results show that WTDGA increase convergence speed and reduce steady state errors.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.512-515
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• 1997

In this paper the fuzzy-genetic controller for path-tracking of WMRs is proposed. Fuzzy controller is implemented to adaptive adjust the crossover rate and mutation rate, and genetic algorithm is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-genetic controller is effective.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.9
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• pp.139-145
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• 1997

This study is concerned with the optimizing method of control parameters for a hydraulic speed control system by using genetic algorithms which are general purpose search algorithms based on natural evolution and genetics. It is shown that the genetic altorithms satisfactorily oiptimized control gains of the PI speed control system of an electrohydraulic servomotor and that optimization of control para- meters can be achived without much experience and knowledge for tuning. It is also shown that optimal gains may be determined from fitness distribution curves plotted in given gain spaces.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.207-212
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• 2003

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.181-186
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• 2004

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.