Model predictive control

List of Articles Model predictive control

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1 - Optimal Operation of Energy Hub Using Model Predictive Control
Z. Hashemi A. Ramezani M. Parsa-Moghaddam
20.1001.1.16823745.1395.14.3.8.4

Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for u More

Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for ultimate consumers. In this paper a state space model for EH system is proposed. Due to the dynamic behavior loads and the price uncertainties, a Model Predictive Control approach is suggested for optimal performance. The proposed method is studied on a EH that consists of transformer, boiler, CHP, electrical and heat storages considering demand side management. Finally, the simulation results depicts to demonstrate the effectiveness of the proposed method for optimal operation of the EH. Manuscript profile
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2 - Distributed Control Scheme Based on Model Predictive Control for Supplying Power in an Isolated DC Microgrid
Arash Abedi Behrooz Rezaie Alireza Khosravi مجید شهابی
20.1001.1.16823745.1399.18.4.5.1

In this paper, a control scheme is presented for an isolated DC microgrid including wind turbine connected to permanent magnet synchronous generator, electrical energy storage unit, and variable electrical loads. Energy sources are connected to a common bus through DC b More

In this paper, a control scheme is presented for an isolated DC microgrid including wind turbine connected to permanent magnet synchronous generator, electrical energy storage unit, and variable electrical loads. Energy sources are connected to a common bus through DC buck and buck-boost converters. The local distributed controllers are located in the first control layer. These controllers are designed based on a Lyapunov stability analysis and thereby its stability is guaranteed. Moreover, the current and voltage, injected to the network, are adjusted by controlling the switching functions of the converters. The decentralized secondary controllers determine the contribution of the local units for supplying the local loads. In this control layer, a model predictive controller for the wind generation unit as well as a proportional-integral controller for preserving the bus voltage are proposed to determine the reference currents for the local controllers. In addition to the practical simplicity, complete isolation of the secondary controllers, minimum requirements to data transfer, and providing a control structure without any need to change in development plan are the important advantages of the proposed control scheme. The performance of the controllers is investigated and verified using the simulations in MATLAB software performed for different cases. Manuscript profile
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3 - Model Predictive Control of Permanent Magnet Synchronous Machine Based on Finite and Continuous Control Sets in Two Functional Quarters
ehsan ghasemi madani Mohammad Reza Alizadeh Pahlavani Arash Dehestani Kolagar
20.1001.1.16823745.1401.20.1.9.3

In this paper, two schemes of model predictive control (MPC) method, named finite control set model predictive control (FCS-MPC) and dead-beat model predictive control (DB-MPC) as a continuous control set model predictive control (CCS-MPC) are applied and compared to co More

In this paper, two schemes of model predictive control (MPC) method, named finite control set model predictive control (FCS-MPC) and dead-beat model predictive control (DB-MPC) as a continuous control set model predictive control (CCS-MPC) are applied and compared to control the current of a permanent magnet synchronous machine in energy recovery mode for the use of electric vehicles. The FCS-MPC strategy selects the optimal voltage vector and applies the control pulses directly to the inverter without using any modulators. In other side, DB-MPC is implemented through space vector pulse width modulation (SVPWM). The performance and results of both types of control strategies are extracted and compared using MATLAB Simulink software. The comparisons are made mainly in steady state and transient modes. Both control strategies are applied to a permanent magnet synchronous machine with the same parameters and with the same operating mode. The results show that the current steady state fluctuation is further reduced in the DB-MPC strategy and the transient state response is faster in the FCS-MPC strategy. Manuscript profile
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4 - Finite-Control-Set Model Predictive Control of an Active Front-End Rectifier with Dynamic References and Comparison with MPDPC Method
Ayyoub Keshvari Mohammad Reza Alizadeh Pahlavani Arash Dehestani Kolagar
20.1001.1.16823745.1402.21.1.6.7

In this paper, a finite-control-set model predictive control method is presented for closed loop control of an active front-end rectifier. The method used has a discrete-time function and does not require any additional modulators. The interesting point in the control a More

In this paper, a finite-control-set model predictive control method is presented for closed loop control of an active front-end rectifier. The method used has a discrete-time function and does not require any additional modulators. The interesting point in the control algorithm is how to control the dynamic references. The control strategy is able to provide proper references for source active power and DC voltage, without the need for additional control loops. In order to better understand the performance, the proposed control method is compared with the model predictive direct power control (MPDPC) method. The results obtained using Matlab/Simulink software show that the proposed method, while having all the capabilities of the MPDPC method, including proper tracking of active power and DC voltage and low current THD, by removing the PI controller and its related disadvantages, it has better stability and faster transient response. Manuscript profile
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5 - Multi-Objective Predictive Control of Two-Motor Drive System with Five-Leg Inverter
Reza Mohammadi Nik Mohammad Reza Alizadeh Pahlavani Arash Dehestani Kolagar

Dual-motor drive systems have been widely favored due to many advantages, including reduced dimensions and cost. In this paper, a model-based predictive control (MPC) method for a dual-motor drive system fed by a five-leg inverter (FLI) is introduced. Among the advantag More

Dual-motor drive systems have been widely favored due to many advantages, including reduced dimensions and cost. In this paper, a model-based predictive control (MPC) method for a dual-motor drive system fed by a five-leg inverter (FLI) is introduced. Among the advantages of the MPC method, the independent and fast tracking of reference control variables and the elimination of cascade control structures dependent on the modulator for multi-motor systems can be mentioned. PI loops have disadvantages such as delayed time response, as well as design limitations of PI coefficients due to the FLI structure. In this paper, using the predictive control method based on the multi-objective model proportional to speed and current (MOMPC), the PI control loops have been removed. One of the challenges of this structure is how to allocate the DC link voltage to the motors. For this purpose, by defining the duty cycle corresponding to the steady-state voltage of the motors, the DC link voltage of the inverter is shared between the motors. In addition, by using this method, the control objectives of two motors become independent from each other, which reduce the torque ripple and current ripple of the motors. Manuscript profile

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List of Articles Model predictive control