• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.585-588
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• 1996

The conventional output feedback robust control designs are very useful for systems under parameter perturbation and uncertain disturbance. However these designs are very complicated and not easily implemented for industrial applications. So, this paper proposes a robust PID controller design method via genetic searching algorithm.

• Journal of Convergence Society for SMB
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• v.4 no.1
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• pp.31-39
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• 2014

PID auto-tuning controller was designed via fuzzy logic. Typical values such as error and error derivative feedback were changed as heuristic expressions, and they determine PID gain through fuzzy logic and defuzzification process. Fuzzy procedure and PID controller design were considered separately, and they are combined and analyzed. Obtained auto-tuning PID controller by Fuzzy Logic showed the ability for less than 3rd order plant control. We also applied to reference tracking problem with the designed auto-tuning scheme.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.425-434
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• 2001

In this paper, a series of processes to configure a feedback control system by using a PID controller in a programmable logic controller (PLC). The PLC (SIMATIC S7-400) with a PID module (FM455C) is connected by online to an IBM PC with the Windows environment, which serves as a PLC programmer. PID controllers including P/PD/PI controllers have been designed in order to show design procedures, and finally, a PID controller for the plant of cart system. Performances of the control system have been investigated by the MATLAB simulation, the simulation in the PLC programmer. Physical performances have been recorded and examined for the real cart system.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.89-91
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• 2004

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal is convergence speed progress about applied gain of PID controller using the neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.452-456
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• 1988

A PID controller must need not only good servo response but also little operation of a control valve. We suggest a direct pole-placement PID self-tuning algorithm using the structure of derivative-of-output controller and Bezout identity. This algorithm can much reduce the change of output of controller and well follow the desired trajectory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1874-1882
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• 2015

In this paper, robust backstepping controller for IPMSM is proposed based on the PID integral sliding mode control. Because of the unmatching condition of load, the sliding mode control is difficult to be used for IPMSM without backstepping. However, the backstepping control has the difficulty of deriving error dynamics which is derived by differentiating the previous input. This difficulty is avoided by adopting PID as a nominal controller for the integral sliding mode control. The proposed controller can be achieved easily by adding integral sliding controller to the conventional PID controller.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.19-27
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• 2005

For continuous communication between moving vehicles such as satellites and unmanned aerial vehicles, an antenna system having at least more than 2-axes is needed. When the antenna is mounted on a moving vehicle such as ground vehicle, ship and so on, a stabilization and tracking system must be equipped to compensate the roll, pitch and yaw motion of the vehicle. The performance of stabilization and tracking system mainly depends on the servo control system that driving the antenna pedestal. Therefore, in this paper, a Fuzzy-PID controller for a stabilization and tracking system of a 2-axes antenna was designed and the performance was verified. To verify the verification of designed servo control system, the performance of the conventional PID controller and that of the Fuzzy-PID controller, designed by the same PID control gains, was compared.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.627-630
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• 2004

The PID type controller has been widely used in industrial application due to its simply control structure, ease of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. This paper presents a hybrid fuzzy logic proportional plus conventional integral derivative controller (fuzzy P+ID). In comparison with a conventional PID controller, only one additional parameter has to be adjusted to tune the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. Finally, the proposed hybrid fuzzy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.85-89
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• 2004

Robust control for feedback control system is needed according to the highest precision of industrial automation. However, when a neural network feedback control system has an effect of disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, hybrid control method of neural network controller and PID controller is presented. A neural network controller is operated as a main controller, a PID controller is a assistant controller which operates only when some undesirable phenomena occur, e.q., when the error hit the boundary of constraint set. The robust control function of neural network-PID hybrid controller is demonstrated by speed control of Motor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.88-95
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• 2006

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They we difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.