• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.427-438
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• 1991

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic system is preented. In the learning control structure, the control input converges globally and asymtotically to the desired input as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of the time-duration of trajectories, it may be achieved with system trajectories with small duration. In addition, the proposd learning control schemes are applicable to time-varying parametric systems as well as time-invariant systems, because the parameter estimation is performed at each fixed time along the iteration. In the parameter estimator, the acceleration information as well as the inversion of estimated inertia matrix are not used at all, which makes the proposed learning control schemes more feasible.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.279-281
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• 1999

This paper addresses the issues of the parameter error detection and identification in power system. The parameter error identification is carried out as part of the state estimation procedure. The weighted least absolute value(WLAV) estimation method is used for this procedure. The standard formulation of the state estimation problem is modified to include the effects of the parameter errors as well. A two step procedure for the detection and identification of faulted parameters is proposed. Supporting examples are given using IEEE 14 bus system.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.2
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• pp.164-171
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• 2009

This paper presents new learning algorithm of dynamic Bayesian networks (DBN) by means of constrained least square (LS) estimation algorithm and gradient descent method. First, we propose constrained LS based parameter estimation for a Markov chain (MC) model given observation data sets. Next, a gradient descent optimization is utilized for online estimation of a hidden Markov model (HMM), which is bi-linearly constructed by adding an observation variable to a MC model. We achieve numerical simulations to prove its reliability and superiority in which a series of non stationary random signal is applied for the DBN models respectively.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.10
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• pp.727-732
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• 1988

In this paper, a parameter estimation method of linear systems with bounded output disturbances is studied. The bound of the disturbances is assumed to known Weighting factors are proposed to modify LS(Least Square) algorithm in the parameter estimation method. The conditions of weighting factors are given so that the estimation method has good convergence properties. This condition is more relaxed form than other known conditions. The compensation term in the estimation equations is represented by a function of the output prediction error and this function should lie in a specified region on x-y plane to satisfy these conditions of weighting factors. A set of weighting factor is selected and an algorithm is proposed using this set of weighting factor. The proposed algorithm is compared with another existing algorithm by simulation and its performance in parameter estimation id discussed.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.79-86
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• 2005

In this paper, an estimator scheme for the rotor speed and flux of an induction motor is proposed on the basis of a fourth-order electrical model. It is assumed that only the stator currents and voltages are measurable, and that the stator currents are bounded. There are a number of common terms in the motor dynamics, and this is utilized to find a simple error model involving some auxiliary variables. Using this error model, the state estimation problem is converted into a parameter estimation problem assuming that the rotor speed is constant. Some stability properties are given on the basis of Lyapunov analysis. In addition, the rotor resistance, which varies with the motor temperature, can also be estimated within the same framework. The effectiveness of the proposed scheme is demonstrated through computer simulations and experiments.

• Journal of Power Electronics
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• v.18 no.1
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• pp.300-308
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• 2018

An online estimation method for wireless power transfer (WPT) systems is presented without using any measurement of the secondary side or the load. This parameter estimation method can be applied with a controlling strategy that removes both the receiving terminal controller and the wireless communication. This improves the reliability of the system while reducing its costs and size. In a wireless power transfer system with an LCCL impedance matching circuit under a rectifier load, the actual load value, voltage/current and mutual inductance can be reflected through reflected impedance measuring at the primary side. The proposed method can calculate the phase angle tangent value of the secondary loop circuit impedance via the reflected impedance, which is unrelated to the mutual inductance. Then the load value can be determined based on the relationships between the load value and the secondary loop impedance. After that, the mutual inductance and transfer efficiency can be computed. According to the primary side voltage and current, the load voltage and current can also be detected in real-time. Experiments have verified that high estimation accuracy can be achieved with the proposed method. A single-controller based on the proposed parameter estimation method is established to achieve constant current control over a WPT system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.956-962
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• 2013

Recently, sensorless controls, which eliminate position and speed sensor in a permanent magnet synchronous motor drive, have been much studied. Most sensorless control algorithms are based on the back-EMF and speed estimations which are obtained from the voltage equations. Therefore, the sensorless control performance is largely affected by the parameter errors of a motor. This paper investigates a novel adaptive control with the parameter error compensation for the speed sensorless control of a permanent magnet synchronous motor. The proposed parameter estimation is obtained from the d-axis current error between the real and estimated currents. The proposed algorithm is verified through the simulation and experimentation.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.289-297
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• 2014

This paper proposed a novel high performance sensorless control scheme for IPMSM based on an extended nonlinear state observer. The gain-matrix of the observer has been derived by using state linearization method. Steady state errors in estimated rotor position and speed due to parameter inaccuracy have been analyzed, and an equivalent flux error is defined to represent the overall effect of parameter errors contributing to the wrong convergence of the estimated rotor speed as well as rotor position. Then, an online compensation strategy was proposed to limit the estimation errors in rotor position and speed. The effectiveness of the extended nonlinear state observer is validated through simulation and experimental test.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.