• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.945-948
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• 2003

In this paper, we propose a robust PID control method with neural network to minimize the influence of the disturbance to happen in the system. The proposed method, the neural network filters out the disturbance of control system. The plant input which a disturbance is included is compensated to the output of neural network and the plant is controlled only PID controller. Through the DC motor control simulation and MM-LDM position control experiment, we could confirm the proposed method is robust at the disturbance in control system.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.247-253
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• 1999

In this paper predictive PID control system using neural network (NNPPID) is proposed to control temperature system. NNPPID is composed of neural network predictor forecasts the future output of plant based on the present input and output of plant. Neural self-tuner yields parameters of PID controller. Experiments prove that NNPPID temperature control system has better performance than conventional PID control.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.159-167
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• 2000

During the operation of crane system in the container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances and weight change. In this paper, we present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control. Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.9-15
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• 1999

A piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, the piezoelectric actuator lacks in repeatability because of its inherently high hysteresis characteristic between voltage and displacement. In this paper, a controller is proposed to compensate the hysteresis nonlinearity. The controller is composed of a PID and a neural network part in parallel manner. The output of the PID controller is used to teach the neural network controller by the unsupervised learning method. In addition, the PID controller stabilizes the piezoelectric actuator in the beginning of the learning process, when the neural network controller is not learned. However, after the learning process the piezoelectric actuator is mainly controlled by the neural netwok controller. In this paper, the excellent tracking performance of the proposed controller was verified by experiments and was compared with the classical PID 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.18 no.3
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• pp.503-509
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• 2014

In this paper, a PID controller with adaptive neural network compensator is proposed to control the formations of mobile robot. The control system is composed of a kinematic controller based on the leader-following robot and dynamic controller for considering the dynamics of the mobile robot. The dynamic controller is constituted by a PID controller and the adaptive neural network compensator for improving the performance and compensating the change in dynamic characteristics. Simulation results show the performance of the PID controller and the neural network compensator for the circular trajectory and linear trajectory. And it is verified that by improving the performance of a PID controller via the adaptive neural network compensator, the following robot's tracking performance is improved.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.46-51
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• 2004

The problem of reducing the effect of an unknown disturbance on a dynamical system is one of the most fundamental issues in control design. We propose a robust PID (Proportional Integral Derivative) control method with neural network for improving the performance due to the rejection of an unknown disturbance. The proposed system consists of a model of the plant, a conventional PID controller and a multi-layer neural network, and is composed of two loop; the first loop enables the system to achieve stability of system, the second loop rejects an unknown disturbance. Simulation and experiment results show that the proposed method improves considerably on the performance of the conventional PID control method and the typical IMC method using neural network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.111-116
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• 1998

Robust control for DC servo motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, PID-neural network hybrid control method for motor control system is presented. The output of neural network controller is determined by error and rate of error change occurring in load disturbance. The robust control of DC servo motor using neural network controller is demonstrated by computer simula tion.a tion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.536-544
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• 2011

In this paper, a controller for two wheeled inverted pendulum type robot is designed to have more stable balancing capability than conventional controllers. Traditional PID control structure is chosen for the two wheeled inverted pendulum type robot, and proper gains for the controller are obtained for specified user's weights using trial-and-error methods. Next a neural network is employed to generate PID controller gains for more stable control performance when the user's weight is arbitrarily selected. Through simulation studies we find that the designed controller using the neural network is superior to the conventional PID controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.29-38
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• 1998

This paper proposes the neural network self-tuning PID controller for the load frequency control of 2- areas power system, namely, the prompt convergence of frequency and tie-line power flow deviation. The neural network applied to computer simulation consists of neurons of two inputs, ten hiddens and tliree outputs layer. Neurons of two inputs layer receive the error and its change rate of the system and cutputs layer consists of three neurons for the parameters of the PID controller. The simulation results shows that the proposed neural network self-tuning PID controller is superior to conventional control t~:chniques(Optimal, PID) in dynamic response and control performance.