• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2275-2278
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• 2005

In this paper, a control algorithm for a servo manipulator is focused on. A servo manipulator system has been developed for remotely handling radioactive materials in a hot cell. It is driven by servo motors. The torque from a servo motor is transferred through a reducer to the corresponding axis. The PID control algorithm is a simple and effective algorithm for such application. However, since friction degrades the algorithm's performance, friction has to be considered and compensated. The major aberrations are the positional tracking errors and the limit cycle. The authors have considered a switching term to a conventional PID algorithm to reduce the friction's effect. It has been tested by a hardware test.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.239-245
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• 2012

This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.353-360
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• 2002

Methods for automatic tuning of PID controllers have been on of the results of the active research on control. The proposed controller also is auto-tuning of PID controller The proposed immune algorithm has an uncomplicated structure and memory-cell mechanism as the optimization algorithm which imitates the principle of humoral immune response. We use the proposed algorithm to solve optimization of PID controller parameters. Up to now, the applications of immune algorithm have been optimization problems with non-varying system parameters. Therefore the usefulness of memory-cell mechanism in immune algorithm is without. And research of memory-cell mechanism does not give us entire satisfaction. This paper proposes the immune algorithm using a memory-cell mechanism which can be the application of system with nonlinear varying parameters. To verify performance of the proposed immune algorithm, the speed control of nonlinear DC motor are performed. The results of Computer simulations represent that the proposed immune algorithm shows a fast convergence speed and a good control performances under the varying system parameters.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.4
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• pp.187-194
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• 2002

Recently, the application of the linear machine far industrial field is remarkable increased, especially for the gantry machine, machine tool system and CNC. In these application fields, high dynamics position control performance Is essentially required in both the steady and the transient state. This pacer presents simple but powerful position control loop based on traditional PID controller. The presented position control algorithm, named 'Unified PID Position Controller'has great features for the linear machine drives such as no over-shoot phenomena and simple gain tuning strategy. Through the experimental results with commercial linear motors, it is shown that the proposed algorithm has excellent dynamics suitable fur linear motions.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.85-90
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.5
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• pp.721-728
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• 2009

PID control is widely used to control stable processes, however, PID control for unstable processes is less common. In this paper, systematic tuning methods are derived to tune the PID controller for unstable FOPTD(Forst Order Plus Time Delay) processes. The proposed PID controllers for set-point tracking and disturbance rejection problem are tuned based on minimizing the performance indexes (IAE, ISE, ITAE) using a real-coded genetic algorithm. Simulation example is given to illustrate the set-point tracking and disturbance rejection performance of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.107-113
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• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.3
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• pp.93-101
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• 2016

This study presents a simple genetic algorithm to systematically design a PID controller for a hydraulic positioning system operated by a proportional solenoid valve. The inverse dead-zone compensator with nonlinear characteristics is used to cancel out the dead-zone phenomenon in the hydraulic system. The object function considering overshoot, settling time, and control input is adopted to search for optimal PID gains. The designed PID controller is compared with the LQG/LTR controller to check the performance of the hydraulic positioning system in the time and frequency domains. The experimental results show that the hydraulic servo system with the proposed PID controller responds effectively to the various types of reference input.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.171-174
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• 2007

A realization for FF-PID(Feed-Forward PID) controlling system with backward propagation algorithm and image pattern recognition is presented in this paper. The pattern recognition used backward propagation of nervous network is teaming. FF-PID is enhanced the response characteristic of moving image by using the controlling value which is output error for the target value of nervous system. In conclusion of experiment, the system is shown that the response is worked as 2.7sec that is enhanced round 15% in comparison with general difference image algorithm. The system is able to control a moving object with effect.

• Journal of Korean Port Research
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• v.14 no.2
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• pp.187-197
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied to the driving control of the autonomous guided vehicle(AGV). When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged by the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined through this off-line manner, these parameters are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted more accurately through the on-line fine tuning. The experiment for the control of steering and speed of AGV is performed. The results show that the proposed controller provides better performances than other conventional controllers.