• Journal of the Korea Convergence Society
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• v.4 no.3
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• pp.43-50
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• 2013

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.543-550
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• 1998

From the power plant in a steel plant, environment pollutants such as $SO_x$, $NO_x$, CO and $CO_2$ are emitted by combustion reactions of the fuels which are by-product gases, oil and liquefied natural gas(LNG). To reduce the amounts of the pollutants, it is important to build a predictive model for the emission of the pollutants. In this paper, model that predict the amounts of generated pollutants for the used fuel is developed by using Gibbs free energy minimization method[1] with the temperature correction technique. For some data set, the calculation results from this model are compared with the real emission amounts of $SO_x$, $NO_x$, and the result of the calculation by both ASPEN PLUS which is a commercial simulation software. This model shows good results and can be applied to other power plants.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1004-1011
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• 2016

The inaccurate model parameters in the predictive current control of surface-mounted permanent magnet synchronous motor (SMPMSM) affect the current dynamic response and steady-state error. This paper presents a model parameter correction algorithm based on the relationship between the errors of model parameters and the static errors of dq-axis current. In this correction algorithm, the errors of inductance and flux are corrected in two steps. Resistance is ignored. First, the proportional relations between inductance and d-axis static current errors are utilized to correct the error of model inductance. Second, the flux is corrected by utilizing the proportional relations between flux and q-axis static current errors under the condition that inductance is corrected. An experimental study with a 100 W SMPMSM is performed to validate the proposed algorithm.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.8-15
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• 2024

This paper proposes a linear model predictive control of 6-DOF remotely operated underwater vehicles using nonlinear robust internal-loop compensator (NRIC). First, we design a integrator embedded linear model prediction controller for a linear nominal model, and then let the real model follow the values calculated through forward dynamics. This work is carried out through an NRIC and in this process, modeling errors and external disturbance are compensated. This concept is similar to disturbance observer-based control, but it has the difference that H_∞ optimality is guaranteed. Finally, tracking results at trajectory containing the velocity discontinuity point and the position tracking performance in the disturbance environment is confirmed through the comparative study with a traditional inverse dynamics PD controller.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2231-2242
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• 2016

A simplified model predictive control method is presented in this paper. This method is based on a future reference voltage vector for a three-phase four-leg voltage source inverter (VSI). Compared with the three-leg VSIs, the four-leg VSI increases the possible switching states from 8 to 16 owing to a fourth leg. Among the possible states, this should be considered in the model predictive control method for selecting an optimal state. The increased number of candidate switching states and the corresponding voltage vectors increase the calculation burden. The proposed technique can preselect 5 among the 16 possible voltage vectors produced by the three-phase four-leg voltage source inverters, based on the position of the future reference voltage vector. The discrete-time model of the future reference voltage vector is built to predict the future movement of the load currents, and its position is used to choose five preselected vectors at every sampling period. As a result, the proposed method can reduce calculation load by decreasing the candidate voltage vectors used in the cost function for the four-leg VSIs, while exhibiting the same performance as the conventional method. The effectiveness of the proposed method is demonstrated with simulation and experiment results.

• Journal of Power Electronics
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• v.15 no.1
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• pp.202-215
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• 2015

This paper investigates the stability and performance of model predictive controlled active-front-end (AFE) rectifiers for energy storage systems, which has been increasingly applied in power distribution sectors and in renewable energy sources to ensure an uninterruptable power supply. The model predictive control (MPC) algorithm utilizes the discrete behavior of power converters to determine appropriate switching states by defining a cost function. The stability of the MPC algorithm is analyzed with the discrete z-domain response and the nonlinear simulation model. The results confirms that the control method of the active-front-end (AFE) rectifier is stable, and that is operates with an infinite gain margin and a very fast dynamic response. Moreover, the performance of the MPC controlled AFE rectifier is verified with a 3.0 kW experimental system. This shows that the MPC controlled AFE rectifier operates with a unity power factor, an acceptable THD (4.0 %) level for the input current and a very low DC voltage ripple. Finally, an efficiency comparison is performed between the MPC and the VOC-based PWM controllers for AFE rectifiers. This comparison demonstrates the effectiveness of the MPC controller.

• Journal of Power Electronics
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• v.17 no.3
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• pp.610-620
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• 2017

The Finite Control Set-Model Predictive Controller (FCS-MPC) for quasi Z-Source Inverters (qZSIs) is designed to reduce the number of sensors by proposing a current observer for the inductor current. Unlike the traditional FCS-MPC algorithm, the proposed model removes the inductor current sensor and observes the inductor current value based on the deposited prior optimized state as well as the capacitor voltage during this state. The proposed observer has been validated versus a typical MPC. Then, a comparative study between the proposed Modified Finite Control Set-Model Predictive Controller (MFCS-MPC) and a linear PID controller is provided under the same operating conditions. This study demonstrates that the dynamic response of the control objectives by MFCS-MPC is faster than that of the PID. On the other hand, the PID controller has a lower Total Harmonic Distortion (THD) when compared to the MFCS-MPC at the same average switching. Experimental results validate both methods using a DSP F28335.

• Journal of the Korean Society of Safety
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• v.21 no.4 s.76
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• pp.25-32
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• 2006

LonWorks over IP(LonWorks-IP) virtual device network(VDN) is an integrated form of LonWorks device network and IP data network. In especially real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. The time delay in servo control on LonWorks-IP based VDN has highly stochastic nature. LonWorks-IP based VDN induced transmission delay deteriorates the performance and stability of the real-time distributed control system and can't give an effective preventive and predictive maintenance. In order to guarantee the stability and performance of the system, and give an effective preventive and predictive maintenance, LonWorks-IP based VDN induced time-varying uncertain time delay needs to be predicted and compensated. In this paper new Pill control scheme based on Smith predictor, disturbance observer and band pass filter is proposed and tested through computer simulation about position control of DC servo motor. It is shown that how can the proposed control scheme be designed to minimize the effects of uncertain varying time delay and model uncertainties. The validity of the proposed control scheme is compared and demonstrated with the comparison of internal model controllers(IMC) based on Smith predictor with and without disturbance observer.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• Fire Science and Engineering
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• v.17 no.2
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• pp.50-55
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• 2003

The lower flash points for the binary system, n-butanol+n-decane, were measured by Pensky-Martens closed cup tester. The experimental results showed the minimum in the flash point versus composition curve. The experimental data were compared with the values calculated by the reduced model under an ideal solution assumption and the flash point-prediction models based on the Van Laar and Wilson equations. The predictive curve based upon the reduced model deviated form the experimental data for this system. The experimental results were in good agreement with the predictive curves, which use the Van Laar and Wilson equations to estimate activity coefficients. However, the predictive curve of the flash point prediction model based on the Willson equation described the experimentally-derived data more effectively than that of the flash point prediction model based on the Van Laar equation.