• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1670-1679
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• 1995

In this paper, an optimal variable structure controller with a multilayer neural inverse identifier is proposed. A multilayer neural network with error back propagation learning algorithm is used for construction the neural inverse identifier which is an observer of the external disturbances and the parameter variations of the system. The variable structure controller with the multilayer neural inverse identifier not only needs a small part of a priori knowledge of the bounds of external disturbances and parameter variations but also alleviates the chattering magnitude of the control input. Also, an optimal sliding line is designed by the optimal linear regulator technique and an integrator is introduced for solving the reaching phase problem. Computer simulation results show that the proposed approach gives the effective control results by reducing the chattering magnitude of control input.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.10
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• pp.407-417
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• 1985

This paper describes a new method for the design of adaptive model-following control systems. This design concept is developed using the theory of slide mode. Authors present new results on the sliding control methodology to achieve accurate tracking for a class of multi-input multi-output, time-varying systems in the presence of parameter variations and disturbances. This algorithm can be easily applied to the multivariable control systems and obtained a smoother control signal in comparison to variable structure model following control systems. The design technique is easy and the control structure is simple. The design requires little computational effort. The control system is less sensitive to plant parameter variations and noise disturbances.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.251-260
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• 1998

A DSP-based robust nonlinear speed control of a permanent magnet synchronous motor(PMSM) which is robust to unknown parameter variations and speed measurement error is presented. The model reference adaptive system(MRAS) based adaptation mechanisms for the estimation of slowly varying parameters are derived using the Lyapunov stability theory. For the disturbances or quickly varying parameters. a quasi-linearized and decoupled model including the influence of parameter variations and speed measurement error on the nonlinear speed control of a PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of the nonlinear speed control of a PMSM is designed and compared with the conventional controller. To show the validity of the proposed control scheme, simulations and experimental works are carried out and compared with the conventional control scheme.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
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• v.8 no.4
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.355-362
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• 2005

Deregulations and market practices in power industry have brought great challenges to the system planning area. In particular, they introduce a variety of uncertainties to system planning. New techniques are required to cope with such uncertainties. As a promising approach, probabilistic methods are attracting more and more attentions by system planners. In small signal stability analysis, generation control parameters play an important role in determining the stability margin. The objective of this paper is to investigate power system state matrix sensitivity characteristics with respect to system parameter uncertainties with analytical and numerical approaches and to identify those parameters have great impact on system eigenvalues, therefore, the system stability properties. Those identified parameter variations need to be investigated with priority. The results can be used to help Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) perform planning studies under the open access environment.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1199-1205
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• 1993

It has been well known that the assumption of full state availability is one of the most important restrictions to the practical realization of VSCS. And several attempts to alleviate the assumption had been made. However, it is not easy to find a positive scheme among them. Recently, an output feedback variable structure control system(OFVSCS) was proposed and the effectiveness of the scheme was validated for the disturbance free systems. The purpose of this study is to propose a robust OFVSCS that have the robust properties against process parameter variations and external distrubances by extending the basic OFVSCS and to evaluate its control performances through power system stabilizer design example. The ROFVSCS is composed of dynamic switching function and output feedback switching control inputs that are constructed by the use of the unknown vector modeling technique. With the proposed scheme, existence of sliding mode is guaranteed and any nonzero bias can be suppressed in the face of disturbances and process parameter variations as far as well-known matching condition is satisfied. Due to the fact that the ROFVSCS is driven by small number of measured informations, the practical application of VSCS for the systems with unmeasurable states and for high order systems that conventional schemes cannot be applied, is possible with the proposed scheme. It is noticeable that the implementation cost of VSCS can be considerably reduced without sacrifice of control performances by adopting ROFVSCS since there is no need measure the states with high measurement cost.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1908-1916
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• 2018

This paper proposes a controller design method for suppressing the resonance generated in the slave motor in the middle and low speed operation range, according to the load and parameter differences between two motors, during parallel operation using the master and slave method that controls two surface permanent magnet synchronous motors connected in parallel by a single inverter. The proposed resonance suppression controller is directly obtained by analyzing the resonance characteristics, using the lead controller method. Therefore, it is possible to fundamentally reduce trial and error to set the controller gain. In addition, because the proposed resonance suppression controller was designed as a lead controller, the stability region of the system increased owing to the added zero point, making the system robust with respect to parametric variations. Simulations and experiments confirmed the usefulness of the proposed method and the system's robustness with respect to parametric variations.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.3
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• pp.151-158
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• 2008

The clutch system is a subcomponent of the transmission that is designed to engage and disengage power flow between the engine and the transmission. Recently, the engine power of automobile has been continuously increased because of customer's demand for the bigger one. As the engine power is increased, the vibration transmitted to the hydraulic clutch operating system has been increased. Therefore the demand for the reduction of clutch pedal vibration during the operation has been increased. This paper describes the pressure pulsation reduction characteristics of the damper cylinder which is applied to the hydraulic clutch operating system. And the purpose of this study is to propose an analysis model and investigate the effect of the design variable variations for the hydraulic clutch system. Especially, we studied the effect of damper cylinder parameter variations on the hydraulic clutch system performance.

• Journal of the Korean Solar Energy Society
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• v.25 no.1
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• pp.1-10
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• 2005

In this paper, 150W photovoltaic system using neural network tracker is proposed, the system designed as the normal line of the solar cell always runs parallel the ray of the sun. This design can minimize the cosine loss of the system output results of solar cell are sensitive to the change of weather and insolation condition don't react rapidly to parameter condition change such as system circumstance and deterioration. To achieve precise operation of photovoltaic tracker system using method of intelligent control, Neural Network is used in the design of the photovoltaic tracker system drive. The control performance of this system drive influenced by the environment parameter such as weather condition and motor parameter variations. we used synchronous motor in this tracker and the experimental results show that the fixing system shows 10,159[Wh] and tracking system shows 12,360[Wh] electricity.