• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.434-443
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• 1999

In this paper, neural network predictive PID (NNPPID) control system is proposed to reduce the vibration of building structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the parameters of controller. The neural networks predictor forecasts the future output based on present input and output of building structure. The controller is PID type whose parameters are yielded by neural networks self-tuning algorithm. Computer simulations show displacements of single and multi-story structure applied to NNPPID system about disturbance loads-wind forces and earthquakes.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.35-41
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• 2006

In this paper, we develop anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2-DOF PID controller. The experimental results jar an ATC simulator show that the proposed control scheme guarantees performances, trolley position, sway angle, and settling time in NNP PID controller than other controller. As a result, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.375-378
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• 1991

This paper presents a design method of synchronous generator excitation controller using self-tuning PID algorithm. Controller parameter is determined by using adaptive control theory in order to maintain optimal operation of generator under the various operating conditions. To determine the optimal parameter of controller. minimum variance algorithm using the recursive leastsquare(RLS) indentification method is adopted and the difference between the speed deviation with weighted factor and voltage deviation is used as the input signal of adaptive controller, which provides good damping and conversion characteristics. The results tested on a single machine infinite bus system verify that the proposed controller has better dynamic performances than conventional controller.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.61-72
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• 2005

In this paper, we develop anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller. The experimental results for an ATC simulator show that the proposed control scheme guarantees performances, trolley position, sway angle, and settling time in NNP PID controller than other controller. As a result, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.324-329
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• 1998

In this paper, neural networks predictive PID (NNPPID) control system is proposed to reduce the vibration of structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the optimal parameters of controller. The neural networks predictor forecasts the future outputs based on present input and output of structure. The controller is PID type whose parameters are yielded by neural networks self tuning algorithm. Computer simulations show displacements of multi-story structures applied to NNPPID system about environmental load-wind forces and earthquakes.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1101-1105
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• 2012

This study proposes a method for improvement of PD type fuzzy controller. The method includes self tuner using gradient algorithm that is one of the optimization algorithms. The proposed controller improves simple Takagi-Sugeno type FLC (Fuzzy Logic Control) system. The simple Takagi-Sugeno type FLC system changes nonlinear characteristic to linear parameters of consequent membership function. The simple FLC system could control the system by calibrating parameter of consequent membership function that changes the system response. While the determination on parameter of the simple FLC system works well only partially, the proposed method is needed to determine parameters that work for overall response. The simple FLC system doesn't predict the response characteristics. While the simple FLC system works just like proportional part of PID, our system includes derivative part to predict the next response. The proposed controller is constructed with P part and D part FLC system that characteristic parameter on system response is changed by self tuner for effective response. Since the proposed controller doesn't include integral part, it can't eliminate steady state error. So we include a gain to eliminate the steady state error.

• Journal of Drive and Control
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• v.12 no.4
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• pp.60-70
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• 2015

A front-end loader (FEL) mounted on an agricultural tractor is one of the most commonly used implements for farm work. However, when the tractor carries material using the bucket attached to the FEL on a sloping ground, the materials can spill or roll back over the operator due to the tilted body, thereby requiring the bucket surface to remain level at a constant value regardless of varying slopes. In this study, an active system for controlling the angle of the FEL bucket on a tractor based on the real-time measurement of ground slopes was developed to enable the bucket to constantly remain level. A FEL simulator operated based on an electro hydraulic proportional valve (EHPV) was constructed in the laboratory to develop a proportional-integral-derivative (PID) controller forming a virtual electronic control unit (ECU) on the computer, which could automatically adjust the bucket angles depending on varying input angles while sending SAE-J1939 associated messages via CAN BUS to the EHPV. The different parameter values for the PID controller due to the gravity effect of the bucket were determined using a manual PID tuning method while assuming that the tractor travels on either an ascending slope or a descending slope. The developed PID control-based self-leveling system showed a mean of steady-state errors of within $1^{\circ}$ and a mean of delayed times of ~ 0.8s when the step input of $+20^{\circ}$ was given, implying that the developed system and control algorithm would be effective in maintaining the bucket angle at a certain value. Future studies include the improvement of the control algorithm to reduce such a time delay as well as the application of the developed algorithm to the FEL mounted on a tractor tested at a testing ground.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2278-2282
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• 2003

In this paper the structure of 2-DOF controller based on artificial immune network algorithms has been suggested for nonlinear system. Up to present time, a number of structures of the 2-DOF controllers are considered as 2-DOF (2-Degrees Of Freedom) control functions. However, a general view is provided that they are the special cases of either the state feedback or the modification of PID controllers. On the other hand, the immune network system possesses a self organizing and distributed memory, also it has an adaptive function by feed back law to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation, since antibody recognizes specific antigens which are the foreign substances that invade living creatures. Therefore, it can provide optimal solution to external environment. Simulation results by immune based 2-DOF controller reveal that immune algorithm is an effective approach to search for 2-DOF controller.