• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.421-424
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• 2003

This paper proposes the generalized predictive control(GPC) method of chaotic systems using a self-recurrent wavelet neural network(SRWNN). The reposed SRWNN, a modified model of a wavelet neural network(WNN), has the attractive ability such as dynamic attractor, information storage for later use. Unlike a WNN, since the SRWNN has the mother wavelet layer which is composed of self-feedback neurons, mother wavelet nodes of the SRWNN can store the past information of the network. Thus the SRWNN can be used as a good tool for predicting the dynamic property of nonlinear dynamic systems. In our method, the gradient-descent(GD) method is used to train the SRWNN structure. Finally, the effectiveness and feasibility of the SRWNN based GPC is demonstrated with applications to a chaotic system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.846-853
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• 2018

In this paper, an improved model predictive control (MPC) method is proposed, which reduces the amount of calculations caused by the increased number of candidate voltage vectors with the increased voltage level in multi-level inverters. When the conventional MPC method is used for multi-level inverters, all candidate voltage vectors are considered to predict the next-step current value. However, in the case that the sampling time is short, increased voltage level makes it difficult to consider the all candidate voltage vectors. In this paper, the improved MPC method which can get a fast transient response is proposed with a small amount of the computation by adding new candidate voltage vectors that are set to find the optimal vector. As a result, the proposed method shows faster transient response than the method that considers the adjacent vectors and reduces the computational burden compared to the method that considers the whole voltage vector. the performance of the proposed method is verified through simulations and experiments.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.566-569
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• 2009

In this paper, we propose a less conservative a linear matrix inequality (LMI) condition for the constrained robust model predictive control of systems with input constraints and polytopic uncertainty. Systems with input constraints are represented as perturbed systems with sector bounded conditions. For the infinite horizon control, closed-loop stability conditions are obtained by using a parameter dependent Lyapunov function. The effectiveness of the proposed method is shown by an example.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1400-1403
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• 1996

In this study, we applied the model predictive control(MPC) to Multi-loop control structure. Since MPC has many advantage for MIMO process and constraints handling, it induces the better performance to apply MPC to multi-loop control. And we suggest the advanced method to reduce the calculation load using the wavelet transform. It shows the possibility to substitute the existing PID control based structure with MPC.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.489-492
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• 1999

This paper proposes a control method for three-phase voltage-source PWM converters using only a single current sensor in the DC link. A predictive current controller for the voltage-source PWM converter is used so that all phase currents can be reconstructed in a switching period although one or two of active vectors are applied only for a short time. Compensation of the 2 step delays is also included. In this paper single sensor current control using predictive state observer will be discussed, and investigated experimentally.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.419-424
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• 1992

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. RLSM(recurrsive least square method) with a forgetting factor is used to de the uncertain distance model parameters and for the future disturbance prediction, future process outputs and inputs projected by the process model are used. The simulation results for chemical reactors indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.143-143
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• 2000

A neural net based generalized predictive control(NNGPC) is presented for a hydraulic servo position control system. The proposed scheme employs generalized predictive control, where the future output being generated from the output of artificial neural networks. The proposed NNGPC does not require an accurate mathematical model for the nonlinear hydraulic system and takes less calculation time than GPC algorithm if the teaming of neural network is done. Simulation studies have been conducted on the position control of a hydraulic motor to validate and illustrate the proposed method.

• Nuclear Engineering and Technology
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• v.37 no.2
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• pp.177-184
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• 2005

The Multivariate State Estimation Technique (MSET) is being used in Nuclear Power Plants for sensor and equipment condition monitoring. This paper presents the use of regularization methods for optimizing MSET's predictive performance. The techniques are applied to a simulated data set and a data set obtained from a nuclear power plant currently implementing empirical, on-line, equipment condition monitoring techniques. The results show that regularization greatly enhances the predictive performance. Additionally, the selection of prototype vectors is investigated and a local modeling method is presented that can be applied when computational speed is desired.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.340-343
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• 1995

This paper consists of the position control of induction motor using Generalized Predictive Control. Full order flux observer is also used for the purpose of estimating rotor fluxes. By using Generalized Predictive Control algorithm, the improved position control is realized in this paper. The proposed control method has been implemented by a 32 bit floating point TMS320C31 DSP chip.

• Journal of Power Electronics
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• v.15 no.3
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• pp.712-720
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• 2015

A model predictive current control (MPCC) method that does not employ a cost function is proposed. The MPCC method can decrease common-mode voltages in loads fed by three-phase voltage-source inverters. Only non-zero-voltage vectors are considered as finite control elements to regulate load currents and decrease common-mode voltages. Furthermore, the three-phase future reference voltage vector is calculated on the basis of an inverse dynamics model, and the location of the one-step future voltage vector is determined at every sampling period. Given this location, a non-zero optimal future voltage vector is directly determined without repeatedly calculating the cost values obtained by each voltage vector through a cost function. Without utilizing the zero-voltage vectors, the proposed MPCC method can restrict the common-mode voltage within ± V_dc/6, whereas the common-mode voltages of the conventional MPCC method vary within ± V_dc/2. The performance of the proposed method with the reduced common-mode voltage and no cost function is evaluated in terms of the total harmonic distortions and current errors of the load currents. Simulation and experimental results are presented to verify the effectiveness of the proposed method operated without a cost function, which can reduce the common-mode voltage.