• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2837-2839
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• 2000

We design a stable controller for a mobile robot with variable gains and reference velocity in order to apply the proper gains and reference velocity, which are generated with fuzzy logic in on-line. The stability is guranteed by the Lyapunov theory. The fuzzy logic rules is found in off-line with GA strategy which drives each object function to be the least. The proposed controller is applied smooth path tracking due to the local path planing. Simulation results show robust performances under a different initial conditions.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.11
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• pp.1072-1078
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• 2001

The real time optimal control algorithm of the DDC controller for chilled water and supply air temperature set-point of heating, ventilating, air-conditioning and refrigeration systems has been researched for minimization of the total power which is consumed by the chiller, chilled water pump and air handing unit fan. The study has been done by using TRNSYS program in order to analyze the central cooling system in terms of the environmental variables such as indoor cooling lead and wet-bulb temperature. This optimal control alogorithm saves more energy and is suitable for real time on-line control in comparison with conventional method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.9
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• pp.1137-1153
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• 1995

It is not yet possible to solve the optimal number of neurons in hidden layer at neural networks. However, it has been proposed and proved by experiments that there is a limit in increasing the number of neuron in hidden layer, because too much incrememt will cause instability,local minima and large error. This paper proposes a module neural controller with pattern recognition ability to solve the above trade-off problems and to obtain fast learning convergence speed. The proposed neural controller is composed of several module having Multi-layer Perrceptron(MLP). Each module have the less neurons in hidden layer, because it learns only input patterns having a similar learning directions. Experiments with six joint robot manipulator have shown the effectiveness and the feasibility of the proposed the parallel module neural controller with pattern recognition perceptron.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.141-144
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• 2003

This paper proposes a design method of a CDBC(Continuous-time Deadbeat Controller)system that takes into account the response between the sampling instant and using second-order smoothing elements. The continuous deadbeat controller is composed of a serial integral compensator and a local feedback compensator introduced into the state feedback loop. A DC servo motor is chosen for implementing CDBC algorithm. Especially according to the variable input and disturbance, corresponding CDBC design method is suggested. A Matlab Simulink is used for simulation with the Motor parameter. By computer simulations, control inputs and system outputs are shown to have desirable property such as smoothness.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.67-73
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• 2002

This paper presents a new design method of optimal continuous deadbeat controller by using second-order smoothing elements. The continuous deadbeat controller is made of a serial integral compensator and a local feedback compensator introduced into the state feedback loop. The decision method of the damping factor and the natural angular frequency of the smoothing element is described. A numerical example is given to show how well input-output characteristics are improved. Especially according to the variable input and disturbance, corresponding CDBC design method is suggested. By computer simulations, control inputs and system outputs are shown to have desirable property such as smoothness.

• Journal of Power Electronics
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• v.18 no.1
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• pp.103-115
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• 2018

Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system of electrical vehicles, which may lead to performance degradation or failure, and eventually threaten reliable operation. Therefore, a model-free deadbeat predictive current controller (MFDPCC) for PMSM drive systems is proposed in this study. The data-driven ultra-local model of a surface-mounted PMSM (SMPMSM) drive system that consists of parametric uncertainties and inverter nonlinearity is first established through the input and output data of a SMPMSM drive system. Subsequently, MFDPCC is designed. The performance comparisons and analyses of the proposed MFDPCC, the conventional proportional-integral controller, and the model-based deadbeat predictive current controller for SMPMSM drive systems are implemented via system simulation and experimental tests. Results show the effectiveness and technical advantages of the proposed MFDPCC.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.77-79
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• 2004

Because robot hardware architecture generally is consisted of a few sensors and motors connected to the central processing unit, this type of structure is led to time consuming and unreliable system. For analysis, one of the fundamental difficulties in real-time system is how to be bounded the time behavior of the system. When a distributed control network controls the robot, with a central computing hub that sets the goals for the robot, processes the sensor information and provides coordination targets for the joints. If the distributed system supposed to be connected to a control network, the joints have their own control processors that act in groups to maintain global stability, while also operating individually to provide local motor control. We try to analyze the architecture of network-based humanoid robot's leg part and deal with its application using the CAN(Controller Area Network) protocol.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.3-41
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• 2001

In this paper, we present a decentralized adaptive neural net(NN) controller for the transient stability and voltage regulation of a multimachine power system. First, an adaptively input-output linearizing controller using NN is designed to eliminate the nonlinearities and interactions between generators. Then, a robust control term which bounds terminal voltage to a neighborhood of the operating point within the desired value is introduced using only local information. In addition, we consider input saturation which exists in the SCR amplifier and prove that the stability of the overall closed-loop system is maintained regardless of the input saturation. The design procedure is tested on a two machine infinite bus power system.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• ETRI Journal
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• v.7 no.3
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• pp.26-29
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• 1985

본 고에서는 cost effective 한 LAN(Local Area Network)구현을 위한 망 인터페이스의 기능을 제시하고, VLSI LAN controller를 이용하며, IBM PC를 위한 network interface board의 설계 방안에 대해서 기술하였다.