• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.58-62
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• 2002

This paper proposes a new nonlinear controller to swing-up an inverted pendulum system mounted on a car. This controller considers not only the pendulum but also the displacement of the cart. A single-input multi-output system is considered to control the inverted pendulum by using partial feedback linearization and nonlinear additional input. The asymptotic stability of the system is shown by using Lyapunov function. The simulation results show effectiveness of the proposed controller.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.65-75
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• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.7
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• pp.43-52
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• 2022

Design of a controller for a high-precision servo control system has been a popular topic while finding optimal parameters for multiple controllers is still a challenging subject. In this paper, we propose a practical scheme to optimize multi-parameters for the robust servo controller design by introducing a new cost function and optimization scheme. The proposed design method provides a simple and practical tool for the systematic servo design to reduce the control error with guaranteeing robust stability of the overall system. The reduction of the position error by 24% along with a faster convergence rate is demonstrated using a typical hard disk drive servo controller with 41 parameters.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.2
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• pp.153-160
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• 2013

Control allocation is an important part of a system. It implements the function that map the desired command forces from the controller into the commands of the different actuators. In this paper, the authors present an approach for solving constrained control allocation problem in vessel system by using multi-parametric quadratic programming (mp-QP) algorithm. The goal of mp-QP algorithm applied in this study is to compute a solution to minimize a quadratic performance index subject to linear equality and inequality constraints. The solution can be pre-computed off-line in the explicit form of a piecewise linear (PWL) function of the generalized forces and constrains. The efficiency of mp-QP approach is evaluated through a dynamic positioning simulation for a vessel by using four tugboats with constraints about limited pushing forces and found to work well.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.439-445
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• 2013

This study is on the system that can integrate generator and charger to use as a welder and generator for need. Specifically, it was hybrid generator consisted with select switch to use generator controller and multiple function welder, that function were auto-stopping generator when generator power finish to recharge in recharger battery and Co2 MMA TIG MIG MAG are devide depend on supplying gas. This control system of voltage, high-frequency, motor and gas valve was implemented in order to improve the performance and convenience of existing welders to choose only one or two at a time.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.221-227
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• 2015

In this study, Multi-Array Electrodes (MAE) and Programmable Multi-channel Electrical Stimulator (PMES) were implemented for firing Trigger Points (TPs) of the patient with Myofascial Pain Syndrome (MPS). MAE has 25 Ag/AgCl electrodes arranged in the form of array ($5{\times}5$) fabricated with flexible pad, which are applicable to be easy-attached to curved specific region of the human body. PMES consisted of 25 channels. Each channel was to generate various electric stimulus patterns (ESPs) by changing the mono-phasic or bi-phasic of ESP, On/Off duration of ESP, the interval between ESP, and amplitude of ESP. PMES hardware was composed of Host PC, Stimulation Pattern Editing Program (SPEP), and Multi-channel Electrical Stimulator (MES). Experiments were performed using MAE and PMES as the following. First experiment was performed to evaluate the function for each channel of Sub- Micro Controller Unit (SMCU) in MES. Second experiment was conducted on whether ESP applied from each channel of SMCU in PMES was focused to the electrode set to the ground, after applying ESP being output from each channel of SMCU in PMES to MAE.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.271-276
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• 2000

Optimal neuro-control algorithm is extended to the control of a multi-degree-of-freedom structure. An active mass driver(AMD) system on the top roof used as a controller. The control signals are made by a multi-layer perceptron(MLP) which is trained by minimizing a sub-optimal performance index. The performance index is a function of both the output responses and the control signals. Structure having nonlinear hysteretic behavior is also trained and controlled by using proposed control algorithm. Bothe the time delay effect and the dynamics of hydraulic actuator are included in the simulation. Example shows that optimal neuro-control algorithm can be applicable to the multi-degree of freedom structures.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.286-290
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• 2000

Optimal neuro-control algorithm is extended to the control of a multi-degree-of-freedom structure. An active mass driver(AMD) system on the top roof is used as an exciter. The control signals are made by a multi-layer perceptron(MLP) which is trained by minimizing a sub-optimal performance index. The performance index is a function of both the output responses and the control signals. Structure having nonlinear hysteretic behavior is also trained and controlled by using proposed control algorithm. In training neuro-controller, emulator neural network is not used. Instead, sensitivity-test data are used. Therefore, only one neural network is used for the control system. Both the time delay effect and the dynamics of hydraulic actuator are included in the simulation. Example shows that optimal neuro-control algorithm can be applicable to the multi-degree of freedom structures.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.712-717
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• 2001

This paper investigates the control problem of evaporator superheat, i.e., the difference between the temperature of the refrigerant at the entrance region of an evaporator and that at the exit region, for multi-type air-conditioning/refrigeration systems. Mathematical equations describing the characteristics of compressor, condenser, evaporator, and electronic expansion valve are first derived. Then, the transfer functions from the current input of the electronic expansion valve to wall temperatures of evaporator tube at two-phase region and superheated region, respectively, are derived. The stability and performance of the closed loop system with a PI controller are analyzed by Nyquist stability criterion. Simulation results are provided.