• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1364-1372
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• 2017

In this paper, a problem of event-triggered model predictive control is investigated for continuous-time Takagi-Sugeno (T-S) fuzzy systems with input quantization. To efficiently utilize network resources, event-trigger is employed, which transmits limited signals satisfying the condition that the measurement of errors is over the ratio of a certain level. Considering sampling and quantization, continuous Takagi-Sugeno (T-S) fuzzy systems are regarded as a sector bounded continuous-time T-S fuzzy systems with input delay. Then, a model predictive controller (MPC) based on parallel distributed compensation (PDC) is designed to optimally stabilize the closed loop systems. The proposed MPC optimize the objective function over infinite horizon, which can be easily calculated and implemented solving linear matrix inequalities (LMIs) for every event-triggered time. The validity and effectiveness are shown that the event triggered MPC can stabilize well the systems with even smaller average sampling rate and limited actuator signal guaranteeing optimal performances through the numerical example.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.502-507
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• 2005

The dual-mode strategy has been adopted in many constrained MPC (Model Predictive Control) methods. The size of stabilizable regions of states of MPC methods depends on the size of underlying feasible and positively invariant sets and the number of control moves. The results, however, may perhaps be conservative because the definition of positive invariance does not allow temporal departure of states from the set. In this paper, a concept of periodic invariance is introduced in which states are allowed to leave a set temporarily but return into the set in finite time steps. The periodic invariance can be defined with respect to sets of different state feedback gains. These facts make it possible for the periodically invariant sets to be considerably larger than ordinary invariant sets. The periodic invariance can be defined for systems with polyhedral model uncertainties. We derive a MPC method based on these periodically invariant sets. Some numerical examples are given to show that the use of periodic invariance yields considerably larger stabilizable sets than the case of using ordinary invariance.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.165-175
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• 2015

Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink(R) and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3% during rectifier mode and 3.5% during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1484-1494
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• 2017

We present a two-stage inverter with high step-up conversion ratio engaging modified finite-set Model Predictive Control (MPC) for utility-integrated photovoltaic (PV) applications. The anticipated arrangement is fit for low power PV uses, the calculated efficiency at 150 W input power and 19 times boosting ratio was around 94%. The suggested high-gain dc-dc converter based on Cockcroft-Walton multiplier constitutes the first-stage of the offered structure, due to its high step-up ability. It can boost the input voltage up to 20 times. The 3S current-source inverter constitutes the second-stage. The 3S current-source inverter hires three semiconductor switches, in which one is functioning at high-frequency and the others are operating at fundamental-frequency. The high-switching pulses are varied in the procedure of unidirectional sine-wave to engender a current coordinated with the utility-voltage. The unidirectional current is shaped into alternating current by the synchronized push-pull configuration. The MPC process are intended to control the scheme and achieve the subsequent tasks, take out the Maximum Power (MP) from the PV, step-up the PV voltage, and introduces low current with low Total Harmonic Distortion (THD) and with unity power factor with the grid voltage.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.1-9
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• 2002

Among the state-space description of discrete vent systems, the max-plus algebra is known as one of the effective approach. This paper proposes a model predictive control (MPC) design method based on the max-plus algebra. Several studies related to these topics have been done so far under the constraints that system parameters are constant. However, in practical systems such as production systems, it is common and sometimes inevitable that system parameters vary by each event. Therefore, it is of worth to design a new MPC controller taking account of adjustable system parameters. In this paper, we formulate system parameters as adjustable ones, and they are solved by a linear programing method. Since MPC determines optimal control input considering future reference signals, the controller can be more robust and the operation cost can be reduced. Finally, the proposed method is applied to a production system with three machines, and the effectiveness of the proposed method is verified through a numerical simulation.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.65-67
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• 2008

A new pulse width modulation method based on predictive current control strategy is proposed to modulate matrix converters. The predictive current controller utilizes a discrete-time model to predict the future values of output currents and generates proper duty-ratios ta minimize the output current errors. The proposed method uses continuous carrier and establishes a predictive current controller to predetermine duty ratio signal for directly generating gating signals an thus is named "predictive current control PWM(PCCPWM)". The modulation algorithm nd the required equations are derived by using average concept over one switching period. Thus it can be easily extended to other matrix converter topologies, especially with neutral connections, such as sing le-phase ad two-phase matrix converters. The feasibility and validity of the proposed strategy are verified by computer simulation and experimental results.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.10
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• pp.381-386
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• 2016

An optimization algorithm is developed based on a simulation case of a single family house model equipped with PV arrays. To increase the nationwide use of PV power generation facilities, a market-competitive electricity price needs to be introduced, which is determined based on the time of use. In this study, quadratic programming optimization was applied to minimize the electricity bill while maintaining the indoor temperature within allowable error bounds. For optimization, it is assumed that the weather and electricity demand are predicted. An EnergyPlus-based house model was approximated by using an equivalent RC circuit model for application as a linear constraint to the optimization. Based on the RC model, model predictive control was applied to the management of the cooling load and electricity for the first week of August. The result shows that more than 25% of electricity consumed for cooling can be saved by allowing excursions of temperature error within an affordable range. In addition, profit can be made by reselling electricity to the main grid energy supplier during peak hours.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1006-1017
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• 2015

This paper presents a finite control set model predictive control (FCS-MPC) strategy for the AC/DC matrix converter used in grid-connected battery energy storage system (BESS). First, to control the grid current properly, the DC current is also included in the cost function because of input and output direct coupling. The DC current reference is generated based on the dynamic relationship of the two currents, so the grid current gains improved transient state performance. Furthermore, the steady state error is reduced by adding a closed-loop. Second, a Luenberger observer is adopted to detect the AC input voltage instead of sensors, so the cost is reduced and the reliability can be enhanced. Third, a switching state pre-selection method that only needs to evaluate half of the active switching states is presented, with the advantages of shorter calculation time, no high dv/dt at the DC terminal, and less switching loss. The robustness under grid voltage distortion and parameter sensibility are discussed as well. Simulation and experimental results confirm the good performance of the proposed scheme for battery charging and discharging control.

• Korean Chemical Engineering Research
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• v.46 no.6
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• pp.1043-1051
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• 2008

In polyolefin processes melt index (MI) is the most important controlled variable indicating product quality. Because of the difficulty in the on-line measurement of MI, a lot of MI estimation and correlation methods have been proposed. In this work a new dynamic MI estimation scheme is developed based on system identification techniques. The empirical MI estimation equation proposed in the present study is derived from the $1^{st}$-order dynamic models. Effectiveness of the present estimation scheme was illustrated by numerical simulations based on plant operation data including grade change operations in high density polyethylene (HDPE) processes. From the comparisons with other estimation methods it was found that the proposed estimation scheme showed better performance in MI predictions. Using the model predictive control method based on the present dynamic MI estimation model, MI values are estimated and compared with those of MI setpoints. From the numerical simulation of the proposed control system, it was found that significant reduction of transition time and the amount of off-spec during grade changes were achieved.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.99-104
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• 1991

An adaptive predictive control method for SISO and MIMO plants is proposed. In this method, future predictions of process output based on a bilinear CARIMA model are used to calculate the control input. Also, a classical recursive adaptation algorithm, equation error method, is used to decrease the uncertainty of the process model. As a result of the application on distillation process, the ability of the set-point tracking and the disturbance rejection is acceptable to apply to the industrial distillation processes.