• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.389-391
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• 1994

This paper proposes an approach to control the current amplitude and phase simultaneously. To do this, variable structure control and adaptive parameter estimation arc applied to the current control of a single-phase PWM inverter with unknown R-L series load. The load parameters, R and L, are estimated by using the recursive least square method and these parameters are used to adjust the feedback gains of control input. The inverter system is modelled in a 2nd-order system by treating load current variation caused by inductive component as a disturbance. Simulation and experiment based on the 2nd -order model are done and the results show good dynamic response and low THD.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1210-1212
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• 1987

Adaptive control for robot manipulator controller has been considered as an effective approach because robot dynamic models contain the nonlinearities and uncertainties. This paper present an approach for the position and velocity control of a manipulator by using the seif-tuning type controller for each point. The complicated model manipulator system is modeled by a set of time series difference equation. The parameters of the models are determined by online recursive algorithms. Finally some remarks on the effectiveness and applications of adaptive controller are discussed.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.488-490
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• 1999

This paper describes the design of self-tuning speed controller for DC motor drive system using RLS(Recursive Least Squares) algorithm and Pole-Placement method. The model parameters, related to inertia and damping coefficient of motor, are estimated on-line by using RLS estimation algorithm. And a control signal is calculated by using pole placement method. Simulation and experimental results show that the proposed controller possesses excellent adaptation capability than a conventional PI/IP controller under parameter change.

• Kyungpook Mathematical Journal
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• v.54 no.2
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• pp.293-320
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• 2014

In this paper, global chaos synchronization is investigated for WINDMI (J. C. Sprott, 2003) and Coullet (P. Coullet et al, 1979) chaotic systems using adaptive backstepping control design based on recursive feedback control. Our theorems on synchronization for WINDMI and Coullet chaotic systems are established using Lyapunov stability theory. The adaptive backstepping control links the choice of Lyapunov function with the design of a controller and guarantees global stability performance of strict-feedback chaotic systems. The adaptive backstepping control maintains the parameter vector at a predetermined desired value. The adaptive backstepping control method is effective and convenient to synchronize and estimate the parameters of the chaotic systems. Mainly, this technique gives the flexibility to construct a control law and estimate the parameter values. Numerical simulations are also given to illustrate and validate the synchronization results derived in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.517-524
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• 1998

This paper discusses the development of a reactive power controller using digital signal processing. Digital Signal Processing is the technique of using digital devices to Process continuous signals or data, often in real-time. And DSP algorithms are associated with a discrete time interval between input samples. When one designs a digital filter, one can use a Laplace transform to determine the continuous time frequency response. The corresponding discrete time transform is called Z transform and depends upon discrete samples of the input spaced equally in time. The objectives of this paper are to minimize real power losses and improve the power factor of a given system. Also, the implementation of a direct-form non recursive filter on the TMS320C31 has been described. The application of this microprocessor-based controller using DSP on test system reveals its numerous advantages. Performance and features of the controller for the reactive power control are analyzed.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1033-1047
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• 2009

In this paper, we consider a m-type risk model with Markov-modulated premium rate. A integral equation for the conditional ruin probability is obtained. A recursive inequality for the ruin probability with the stationary initial distribution and the upper bound for the ruin probability with no initial reserve are given. A system of Laplace transforms of non-ruin probabilities, given the initial environment state, is established from a system of integro-differential equations. In the two-state model, explicit formulas for non-ruin probabilities are obtained when the initial reserve is zero or when both claim size distributions belong to the $K_n$-family, n $\in$ $N^+$ One example is given with claim sizes that have exponential distributions.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.285-288
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• 2002

This paper presents external load disturbance compensation that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a deadbeat observer To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller The proposed estimator is combined with a high performance load torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.25-28
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• 1987

Design procedure of adaptive controller with variable load condition is present and applied to velocity control of small, permanent magnet DC servo motor. The state feedback control scheme is adopted and Recursive Least Squares algorithm is used for parameter estimation. In order to reduce the time consuming. In the procedure of adaptation-gain tuning of state feedback controller, approximate curve fitting technique is applied to the relations between load condition and poles of the system, load condition and feedback gains. With this method, fast adaptation can be accomplished. It is shown that this procedure can be applied not only to variable load condition but also to variation of other system constants, for example variation of resistance and inductance etc.. Simulation results is present for both cases - variable inertia load, variable motor resistance to verify performance improvements. This design procedure produces an adaptive con troller which is feasible for implementation with microprocessor by reducing calculation time.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.