• Journal of Satellite, Information and Communications
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• v.12 no.1
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• pp.18-21
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• 2017

In this paper we present IoT compliant HD Digital Signage Controller design using Raspberry Pi. Nowadays, the digital signage system have gained increasing popularity and rapidly growing communications medium that offers a wide range of exciting ways with customers, enhance brand awareness, increase sales and margin uplift by replacing the traditional static signage system. This paper presents a cost-effective IoT based HD Digital Signage Controller (DSC) using R-Pi computer that can use various kinds of displays in order to make the system as efficient and affordable as possible to provide advertisement or other relevant information to people in public areas. The system is designed in such way the information about surrounding related place is taken through sensors and directly uploaded to the internet, and thus can be accessed anytime and displayed on screens by using Raspberry Pi that serves as controller in the system. The design and implementation of the system developed by using python programming language and its hardware consists of sensors, Arduino, Raspberry Pi 3 model B board and the displays. The main targets of this study are build a cost effective Digital Signage system as well as broadcasting information at display contents in public areas and control those digital signs.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.4
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• pp.78-86
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• 2002

In this paper, LMI (Linear Matrix Inequality) based on H$\_$$\infty$/ controller for a lire of sight (LOS) stabilization system. It shows that the proposed controller has more excellent stabilization performance than that of the conventional PI-Lead controller. An H$\_$$\infty$/ control has been also applied to the system for reducing modeling errors and the settling time of the system. The LMI-based H$\_$$\infty$/ controller design is more practical in view of reducing a run-time than Riccati-based H$\_$$\infty$/ controller. This H$\_$$\infty$/ controller is available not only to decrease the gain in PI-Lead control, but also to compensate the identifications for the various uncertain parameters. Therefore, this paper, shows that the proposed LMI-based H$\_$$\infty$/ controller had good disturbance attenuation and reference input tracking performance compared with the control performance of the conventional controller under any real disturbances.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1218-1225
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• 2008

This paper presents the design of a neural network based adaptive control for missile is presented. The application model is Exocet MM40, which is derived from missile DATCOM database. Acceleration of missile by tail Fin control cannot be controllable by DMI (Dynamic Model Inversion) directly because it is non-minimum phase system. So, the inner loop consists of DMI and NN (Neural Network) and the outer loop consists of PI controller. In order to satisfy the performances only with PI controller, it is necessary to do some additional process such as gain tuning and scheduling. In this paper, all flight area would be covered by just one PI gains without tuning and scheduling by applying mixture control technique of conventional controller and NN to the outer loop. Also, the simulation model is designed by considering non-minimum phase system and compared the performances to distinguish the validity of control law with conventional PI controller.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.1-5
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• 2010

We propose an algorithm which can classify the system should use a PI controller, which have a weak high frequency attenuation characteristics near the critical frequency. To classify the system, we use a time delay element to calculate a gain attenuation rate near the critical frequency. The proposed algorithm also can design PI controller with the given magnitude margin and phase margin specification. The proposed algorithm uses time delay element and saturation function to identify the one point information in frequency domain. We justify the proposed algorithm via the simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.65-73
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• 2006

This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of Power Electronics
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• v.15 no.1
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• pp.177-189
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• 2015

The output of the controller is said to exceed the input limits of the plant being controlled when a control system operates in a non-linear region. This process is called the windup phenomenon. The windup phenomenon is not preferable in the control system because it leads to performance degradation, such as overshoot and system instability. Many anti-windup strategies involve switching, where the integral component differently operates between the linear and the non-linear states. The range of state for the non-overshoot performance is better illustrated by the boundary integral error plane than the proportional-integral (PI) plane in windup inspection. This study proposes a PI controller with a separate closed-loop integral controller and reference value set with respect to the input command and external torque. The PI controller is compared with existing conventional proportional integral, conditional integration, tracking back calculation, and integral state prediction schemes by using ScicosLab simulations. The controller is also experimentally verified on a direct current motor under no-load and loading conditions. The proposed controller shows a promising potential with its ability to eliminate overshoot with short settling time using the decoupling mode in both conditions.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.825-827
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• 2001

In this paper, we made mathematical modeling of BLDC motor system and design of PI controller. Using Apache web-server and PHP web-programming, we embodied virtual laboratory for PI controller design of BLDC Motor system by using proportional constants$(K_P)$ and constants of integration$(K_I)$.

• International Journal of Air-Conditioning and Refrigeration
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• v.14 no.4
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• pp.138-146
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• 2006

Recently, a technology based on the proportional integral (PI) control have grown rapidly owing to the needs for the robust capacity of the controllers from industrial building sectors. However, PI controller generally requires tuning of gains for optimal control when the outside weather condition changes. The present study presents the possibility of reinforcement learning (RL) control algorithm with PI controller adapted in the HVAC system. The optimal design criteria of RL controller was proposed in the environment chamber experiment and a theoretical analysis was also conducted using TRNSYS program.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.4
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• pp.33-42
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• 2010

The induction motor drive applied to field oriented control is widely used in industry applications. However, it is deceased performance and authenticity by saturation, temperature changing, disturbance and parameters changing because modeling of induction motor is nonlinear and complex. In order to control variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper proposes self tuning PI controller based on fuzzy-neural network(FNN)-PI controller that is implemented using fuzzy control, neural network, and adaptive fuzzy controller(AFC). Also, this paper proposes estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FNN-PI, AFC and ANN controller. Also, this paper proposes the anlysis results to verify the effectiveness of controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1663-1668
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• 2017

A BLDC motor with higher number of phases has several advantages, compared to the conventional three-phase BLDC motors. It can reduce the commutation torque ripple and the iron loss without increasing the voltage per phase and increase the reliability and power density. Higher number of phases increase the torque-per-ampere ratio for the same machine volume and output power by widening the electrical conduction period. In this paper, the proposed seven-phase BLDC motor drive system is made into several functional modular blocks, so that it can be easily extended to other ac motor applications: back-EMF block, hysteresis current control block, pwm inverter block, phase current block, and speed/torque control block. Also in a system of BLDC motor drive, the PI controller has been widely used in the speed controller because of the simple implementation. To obtain a good speed response in a general drive system using the PI controller, the high bandwidth of a controller is established. therefore, in this paper, a Fuzzy controller is applied to the 7-phase BLDC motor drive system in order to improve the speed control performance. The Fuzzy controller is compared with a conventional PI controller through the experiment with respect to speed dynamic responses. These experimental results show that the Fuzzy controller of the 7-phase BLDC motor drive system is superior over the conventional PI controller. The algorithm using the Fuzzy controller can improve a comfortable ride in the field of high performance 7-phase BLDC motor drive applications.