• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.138-142
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• 2004

This paper proposes the design of the model following control system using the PID control structure. PID control system became model following control by inserting new pre-compensator in order to improve control performance in discrete-time region. Gain of the PID controller needs to be readjusted when response of system changes due to disturbance or load fluctuation. Performance of control system improves by joining neural network to PID control system because performance of control system depends largely on each PID gain in PID control system. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Angular position of DC servo motor is selected as a plant in order to verify control performance in model following control. After it is applied to the position control system, it's performance is verified through computer experiment.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.4
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• pp.278-284
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• 2002

The work presented here provides a control of the supply duct outlet air temperature in PEM (personal environment module) using fuzzy PID controller. In previous work, PID control systems were used, but the result shows that the outlet air temperature and electric heater regulating voltage were oscillated. Fuzzy PID control systems are designed to improve the system response obtained using PID control and implemented experimentally Also, PID controller and fuzzy controller without PID logic are provided to compare the result with that of the fuzzy PID controller. Data obtained shows that the fuzzy PID control system satisfies the design criteria and works proper1y in controlling the supply air temperature. Also it has bettor performance than the previous result obtained using PID control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2554-2561
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• 2015

In this paper, we strengthen the advantages of a simple PID controller as a study on the formation control of mobile robots and propose an adaptive PID controller with robust performance at the dynamics characteristics of following robot. Simulation studies show that the adaptive PID controller has better keeping constant distance and angle such as tracking performance of following robot for the formation control than a conventional PID controller. This is the proposed adaptive PID controller to change the gains is found to represent the best performance. This is able to verify that the performance of the proposed adaptive PID controller is excellent.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.103-108
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• 2009

Robust control for DC 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. In this paper, PID-Expert hybrid control method for motor control system as a compensation method solving this problem is presented. If PID control system is stable, the Expert controller is idle. if the error hits the boundary of the constraint, the Expert controller begins operation to force the error back to the constraint set. The disturbance effect decrease remarkably, robust speed control of DC motor using PID-Expert Hybrid controller is demonstrated by the simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1560-1564
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• 2008

Robust control for DC 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. In this paper, PID-Self Tuning control method for motor control system as a compensation method solving this problem is presented. If the PID control system is stable in the sense that the error is inside the constraint set, the supervisory control is idle. If the error hits the boundary of the constraint, the supervisory controller begins operation to force the error back to the constraint set. We prove that the PID-Self Tuning control system is globally stable in the sense that the error is guaranteed to be within the tolerance limits specified by the system designer.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.68-68
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• 2000

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.699-703
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• 1991

A magnetic levitation system with hybrid magnets, which is composed of permanent magnets and electromagnets, consumes less power than the conventional attraction type system. In this paper, we propose PID controller and PID-Fuzzy controller for hybrid magnet. We first present "constant gap" control technology with PID controller. Secondly, "zero power" control technology with PID-Fuzzy hybrid controller is presented.roller is presented.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.5
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• pp.56-61
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• 2000

Robust control for DC 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 systems.In this paper, PID-Expert hybrid control method for motor control system as a compensation method solving this problem is presented. If PID control system is stable, the Expert controller is idle. if the error hits the boundary of the constraint, the Expert controller begins operation to force the error back to the constraint set. The disturbance effect decrease remarkably, robust speed control of DC motor using PID-Expert Hybrid controller is demonstrated by the simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1871-1880
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• 2011

In this paper, a controller for two wheeled inverted pendulum robot, i.e., Segway type robot that is a convenient and easily handled vehicle is designed to have more stable balancing and faster velocity control compared to the conventional method. First, a widely used PID control structure is applied to the two wheeled inverted pendulum robot and proper PID control gains for some specified weights of users are obtained to get accurate balancing and velocity control by use of experimental trial-and-error method. Next, neural network is employed to generate appropriate PID control gains for arbitrarily selected weight. Here the PID gains based on the trial-and-error method are used as training data. Simulation study has been carried out to find that the performance of the designed controller using the neural network is more excellent than the conventional PID controller in terms of faster balancing and velocity control.