• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.12
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• pp.884-893
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• 1988

Adaptive controllers for linear unknown coefficient system, that is corrupted by disturbance, are designed by parameter adaptation model reference adaptive control(MRAC). This design is stemmed from the Lyapunov direct method. To reduce the model following error and to improve the convergence rate of the design, an indirect-suboptimal control law is derived. Proper compensation for the effects of time-varying coefficients and plant disturbance are suggested. In the design procedure no complete identification of unknown coefficients are required.

• Journal of Power Electronics
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• v.10 no.5
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• pp.518-527
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• 2010

This paper presents a new technique for directly designing a linear digital controller for a single-phase pulse width modulation (PWM) converter systems, based on closed-loop identification. The design procedure consists of three steps. First, obtain a digital current controller for the inner loop system by using the error space approach, so that the power factor of the supply is close to one. The outer loop is composed of a voltage controller, a current control loop including a current controller, a PWM converter, and a capacitor. Then, all the components, except the voltage controller, are identified by a discrete-time equivalent linear model, using the closed-loop output error (CLOE) method. Based on this equivalent model, a proper digital voltage controller is then directly designed. It is shown through PSim simulations and experimental results that the proposed method is useful for the practical design of PWM converter controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.170.1-170
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• 2001

This paper developed the model for electro hydraulic force control system by identification method via ARMAX model. Implementation of Identification is performed on Pin-On-Disk type tribotester. The wear mechanism is an important mechanic property to select a material´s life and a optimum work condition. Pin-on-disk type tribotester is popular wear analysis experimental equipment and its mechanism is that adding a force on a rotating disk to simplify two surface contact´s wear experimental condition. Material´s rotating velocity and eccentricity rotation makes disturbance and it affects adding constant force. To get a high performance of force adding part, DDV(Direct Drive Valve) which has pressure control loop is used. To obtain a tribotester´ s ARMAX model, prediction error method(PEM) is used in case force adding part and rotating part is ...

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.169.1-169
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• 2001

In general, for high performance pressure control system, hydraulic system with electo hydraulic servo valve controls flow rate, it contains many nonlinear term like square-root and change of bulk modulus by flow rate. But, DDV(Direct Drive Valve) contains pressure control loop itself, then it can eliminate nonlinearity and achieve linearity for hydraulic system. In this paper, parameter identification method which uses input and ouput data is applied to obtain DDV's mathematical model and parameter assuming that dynamic characteristic of DDV is first order system. Then, the state feedback controller was designed to implement the force control of hydraulic system , and the control performance was evaluated.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.142-155
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• 2000

A MIMO model reference control scheme incorporating the variable structure theory for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of continuous-time nonlinear dynamics with known or unknown uncertainties. The scheme employs an neural network to identify the plant systems, where the neural network estimates the nonlinear dynamics of the plant. By the Lyapunov direct method, the algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed and it is not necessary to know the exact structure of the system. With the resulting identification model which contains the neural networks, it does not need higher degrees of freedom vehicle model than 3 degree of freedom model. Th proposed scheme is applied to the active four wheel system and shows the validity is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the reduction of yaw rate overshoot of a typical mid-size car improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response and smaller side angle than the 2WS case.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.898-901
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• 1988

Up to now, using adaptive control method, Identification deals with system whose entire state variables and prameters are accessible for measurement. In practical situations, all the state variables cannot be measured and it is impossible to directly apply since the parameters of the system are unknown. Therefore, in this paper, using only input-output data, such a model of the system is not available since the parameters of the system are unknown. this leads to the concept of an adptive observer in which both the parameters and the state variable of the system are identified simultaniously. Lyapunov's direct method and Kalman-Yakubovich (K-Y) lemma are employed to ensure the stability of this schemes. The feature is that the signal and adaptive gain which is generated from filter is imposed upon feedback vector and then state variables and the unknown parameters can be identified. To show the usefulness of the proposed schemes, computer simulation result of unknown second-order system shows the effectiveness of the proposed schems.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.224-226
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• 1993

Multi-layered neural networks offer an exciting alternative for modelling complex non-liner systems. This paper investigates the identification of continuous time nonliner system using neural networks with a single hidden layer. The digital low - pass filter are introduced to avoid direct approximation of system derivatives from sampled data. Using a pre-designed digital low pass filter, an approximated discrete-time estimation model is constructed easily. A continuous approximation liner model is first estimated from sampled input-out signals. Then the modeling error due to the nonlinearity is decreased by a compensator using neural network. Simulation results are given to demonstrate the effective of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1371-1379
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• 2012

This paper presents a method for estimating the contribution of vibration sources in gasoline direct injection engine parts with a multiple-input system. A partial coherence function was used to identify the cause of the linear dependence indicated by an ordinary coherence function. To apply the partial coherence function to vibration source identification in the powertrain system of a gasoline direct injection engine, a virtual model of a two-input and single-output system is simulated. For the validation of this model, the vibration of the powertrain parts was measured by using triaxial accelerometers attached to the selected vibration sources-a high-pressure pump, fuel rail, injector, and pressure sensor. After calculating the partial coherence between each source based on the virtual model, the vibration contribution of the powertrain system is calculated. This virtual model based on the partial coherence function is implemented to determine the quantitative vibration contribution of each powertrain part.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1155-1157
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. Two direct adaptive control methods are applied to a Duffing's equation and the simulation results show the effectiveness of the controllers.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1633-1642
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• 2000

Like many other mechanical dynamic systems, flexible manipulator systems experience stiction or sticking friction, which may cause input-dependent instabilities. Manipulator performance can be enha nced by identifying friction but it is hard and expensive to measure friction by direct and precise sensing of contact displacements and forces. This study addresses the problem of identifying flexible manipulator joint friction. A dynamic model of a two-link flexible manipulator based upon finite element and Lagrange's method is constructed. The dynamic model includes the effects of joint compliances and actuator dynamics. Friction is also incorporated in the dynamic model to account for stick-slip at the joints. Next, the friction parameters are to be determined. The identification problem is posed as an optimization problem to be solved using nonlinear programming methods. A genetic algorithm is used to increase the convergence rate and the chances of finding the global optimum. The identified friction parameters are experimentally verified and it is expected that the identification technique is applicable to a system parameter identification problem associated with a wide class of nonlinear systems.