scholarly journals Advanced Strategy of Speed Predictive Control for Nonlinear Synchronous Reluctance Motors

Machines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 44 ◽  
Author(s):  
Ahmed Farhan ◽  
Mohamed Abdelrahem ◽  
Christoph M. Hackl ◽  
Ralph Kennel ◽  
Adel Shaltout ◽  
...  
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Controller Design ◽  
Control Strategies ◽  
Fem Analysis ◽  
Power Converter ◽  
Unknown Parameters ◽  
Qualitative Comparison ◽  
Finite Control ◽  
Novel Strategy

To gain fast dynamic response, high performance, and good tracking capability, several control strategies have been applied to synchronous reluctance motors (SynRMs). In this paper, a nonlinear advanced strategy of speed predictive control (SPC) based on the finite control set model predictive control (FCS-MPC) is proposed and simulated for nonlinear SynRMs. The SPC overcomes the limitation of the cascaded control structure of the common vector control by employing a novel strategy that considers all the electrical and mechanical variables in one control law through a new cost function to obtain the switching signals for the power converter. The SynRM flux maps are known based on finite element method (FEM) analysis to take into consideration the effect of the nonlinearity of the machine. To clear the proposed strategy features, a functional and qualitative comparison between the proposed SPC, field-oriented control (FOC) with an anti-windup scheme, and current predictive control (CPC) with outer PI speed control loop is presented. For simplicity, particle swarm optimization (PSO) is performed to tune all the unknown parameters of the control strategies. The comparison features include controller design, dynamic and steady-state behaviors. Simulation results are presented to investigate the benefits and limitations of the three control strategies. Finally, the proposed SPC, FOC, and CPC have their own merits, and all methods encounter the requirements of advanced high-performance drives.

scholarly journals Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2325
Author(s):  
Sanaz Sabzevari ◽  
Rasool Heydari ◽  
Maryam Mohiti ◽  
Mehdi Savaghebi ◽  
Jose Rodriguez
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Strategies ◽  
Power Converter ◽  
Performance Criterion ◽  
Parameter Mismatch ◽  
Model Free ◽  
Finite Control ◽  
Free Current ◽  
Definition Of

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

scholarly journals Finite Control Set Model Predictive Control of Six-Phase Asymmetrical Machines—An Overview

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4693 ◽  
Author(s):  
Pedro Gonçalves ◽  
Sérgio Cruz ◽  
André Mendes
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Strategies ◽  
Current Control ◽  
Torque Control ◽  
Current Harmonics ◽  
Electric Drives ◽  
Finite Control ◽  
Control Set

Recently, the control of multiphase electric drives has been a hot research topic due to the advantages of multiphase machines, namely the reduced phase ratings, improved fault tolerance and lesser torque harmonics. Finite control set model predictive control (FCS-MPC) is one of the most promising high performance control strategies due to its good dynamic behaviour and flexibility in the definition of control objectives. Although several FCS-MPC strategies have already been proposed for multiphase drives, a comparative study that assembles all these strategies in a single reference is still missing. Hence, this paper aims to provide an overview and a critical comparison of all available FCS-MPC techniques for electric drives based on six-phase machines, focusing mainly on predictive current control (PCC) and predictive torque control (PTC) strategies. The performance of an asymmetrical six-phase permanent magnet synchronous machine is compared side-by-side for a total of thirteen PCC and five PTC strategies, with the aid of simulation and experimental results. Finally, in order to determine the best and the worst performing control strategies, each strategy is evaluated according to distinct features, such as ease of implementation, minimization of current harmonics, tuning requirements, computational burden, among others.

scholarly journals Coordinated path-following control for networked unmanned surface vehicles

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142093057
Author(s):  
Dong-Liang Chen ◽  
Guo-Ping Liu ◽  
Ru-Bo Zhang ◽  
Xingru Qu
Keyword(s):  
Predictive Control ◽  
Controller Design ◽  
Control Strategies ◽  
Path Following ◽  
Control Performance ◽  
Negative Effects ◽  
Unmanned Surface Vehicles ◽  
Path Following Control ◽  
Control Scheme ◽  
Networked Predictive Control

In this article, the coordinated path-following control problem for networked unmanned surface vehicles is investigated. The communication network brings time delays and packet dropouts to the fleet, which will have negative effects on the control performance of the fleet. To attenuate the negative effects, a novel networked predictive control scheme is proposed. By introducing the predictive error into the control scheme, the proposed control strategy admits some advantages compared with existing networked predictive control strategies, for example, a degree of robustness to disturbances, lower requirements for the computing capacity of the onboard processors, high flexibility in controller design, and so on. Conditions that guarantee the control performance of the overall system are derived in the theoretical analysis. At last, experiments on hovercraft test beds are implemented to verify the effectiveness of the proposed control scheme.

scholarly journals A Comparative Study on Weighting Factor Design Techniques for the Model Predictive Control of Power Electronics and Energy Conversion Systems

2021 ◽  
Author(s):  
Oluleke Babayomi ◽  
Yuzhe Zhang ◽  
yu li ◽  
Yongdu Wang ◽  
Zhen Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Energy Conversion ◽  
Power Electronics ◽  
Predictive Control ◽  
High Performance ◽  
Dynamic Performance ◽  
Power Converter ◽  
Weighting Factor ◽  
Factor Design ◽  
Design Techniques

During the past decade, the model predictive control (MPC) of power electronics and drives has witnessed significant advancements in both dynamic performance and range of applications. However, researchers still encounter challenges with the optimal design of weighting factors, and this lowers the capabilities derivable from MPC. This study first reviews the different weighting factor design techniques proposed in the literature for power electronics and electrical drives (applied to wind/solar energy conversion, microgrids, grid-connected converters and other high performance converter-based systems). They are grouped under heuristic, offline tuning, sequential, and online optimization methods. Next, the study provides real-time hardware-in-the-loop comparative results for the implementation of four weighting factor design techniques on a grid-connected two-level back-to-back power converter-based permanent magnet synchronous generator wind turbine system. Through these laboratory results, the advantages and limitations of the different weighting factor design methods are highlighted.

scholarly journals A Comparative Study on Weighting Factor Design Techniques for the Model Predictive Control of Power Electronics and Energy Conversion Systems

2021 ◽  
Author(s):  
Oluleke Babayomi ◽  
Yuzhe Zhang ◽  
yu li ◽  
Yongdu Wang ◽  
Zhen Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Energy Conversion ◽  
Power Electronics ◽  
Predictive Control ◽  
High Performance ◽  
Dynamic Performance ◽  
Power Converter ◽  
Weighting Factor ◽  
Factor Design ◽  
Design Techniques

During the past decade, the model predictive control (MPC) of power electronics and drives has witnessed significant advancements in both dynamic performance and range of applications. However, researchers still encounter challenges with the optimal design of weighting factors, and this lowers the capabilities derivable from MPC. This study first reviews the different weighting factor design techniques proposed in the literature for power electronics and electrical drives (applied to wind/solar energy conversion, microgrids, grid-connected converters and other high performance converter-based systems). They are grouped under heuristic, offline tuning, sequential, and online optimization methods. Next, the study provides real-time hardware-in-the-loop comparative results for the implementation of four weighting factor design techniques on a grid-connected two-level back-to-back power converter-based permanent magnet synchronous generator wind turbine system. Through these laboratory results, the advantages and limitations of the different weighting factor design methods are highlighted.

scholarly journals Implementation of Neural Predictive Control To Distillation Column

REAKTOR ◽  
2017 ◽  
Vol 10 (1) ◽  
pp. 24
Author(s):  
S. Anwari
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Distillation Column ◽  
Controller Design ◽  
Fundamental Equation ◽  
Small Range ◽  
Unknown Parameters ◽  
Predictive Controller ◽  
Multivariable Models ◽  
Reliable Solution

This paper presents a neural predictive controller that is applied to distillation column. Distillation columns represent complex multivariable system, with fast and slow dynamics, significant interactions and directionality. A phenomenological model (i.e. a model derived from fundamental equation like mass and energy balance) of a distillation column is very complicated. For this reason, classical linear controller, such as PID (Proportional, Integral and Derivative) controller, will provide robustness only over relatively small range operation because of complexity and operation without lack of robustness. In this work, a neural network is developed for modeling and controlling a distillation column based on measured input-outputdata pairs. In distillation column, a neural network is trained on the unknown parameters of the system. The resulting implementationof the neural predictive controller is able to eliminate the most significant obstacles encountered in conventional predictive control application by facilitating  the development of complex multivariable models and providing a rapid, reliable solution to the control algorithm. Controller design and implementation are illustrated for a plant frequently referred to in the literature. Result are given for simulation experiments, which demonstrate the advantage of the neural based predictive controller both at the transient region and at the steady state region to overcome any overshoots.Keywords : neural predictive controller, distillation column, complex multivariable models

scholarly journals Robust Multiobjective Model Predictive Control with Computation Delay Compensation for Electric Vehicle Applications Using PMSM with Multilevel Inverter

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Abdiddaim Katkout ◽  
Tamou Nasser ◽  
Ahmed Essadki
Keyword(s):  
Model Predictive Control ◽  
Electric Vehicle ◽  
Predictive Control ◽  
High Performance ◽  
Dynamic Error ◽  
Permanent Magnet Synchronous Motor ◽  
Algorithm Optimization ◽  
Delay Compensation ◽  
Finite Control ◽  
Discrete Nature

The Three-Level Neutral-Point-Clamped (3L-NPC) inverter fed Permanent Magnet Synchronous Motor (PMSM) drive is an attractive configuration for high performance Electric Vehicle (EV) applications. For such configuration, due to their high performances, the Finite-Control-Set Model Predictive Control (FCS-MPC) is a very attractive control solution. The FCS-MPC scheme is based on the prediction of the future behavior of the controlled variables using the dynamic model of PMSM and the discrete nature of the 3L-NPC inverter. However, the parametric uncertainties and time-varying parameters affect the FCS-MPC algorithm performances. In this paper, robust FCS-MPC controls based on “dynamic error correction” (DEC) and “modified revised prediction” (MRP) are proposed to improve the FCS-MPC robustness without affecting the controller performances and complexity. The proposed strategies are improved also by multiobjective (MO) algorithm optimization and computation delay compensation. The simulation results included prove the performance in robustness and efficiency of the proposed robust FCS-MPC-DEC.

scholarly journals Finite Control Set Model Predictive Control for Paralleled Uninterruptible Power Supplies

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3453
Author(s):  
Tiago Oliveira ◽  
Luís Caseiro ◽  
André Mendes ◽  
Sérgio Cruz
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Power Supplies ◽  
Voltage Waveform ◽  
High Quality ◽  
Uninterruptible Power Supplies ◽  
Finite Control ◽  
Control Set ◽  
Uninterruptible Power

Nowadays, uninterruptible power supplies (UPS) play an important role in feeding critical loads in the electric power systems such as data centers or large communication hubs. Due to the increasing power of these loads and frequent need for expansion or redundancy, UPS systems are frequently connected in parallel. However, when UPS systems are parallel-connected, two fundamental requirements must be verified: potential circulating currents between the systems must be eliminated and the load power must be distributed between the systems according to UPS systems availability. Moreover, a high-quality load voltage waveform must be permanently ensured. In this paper innovative control strategies are proposed for paralleled UPS systems based on Finite Control Set Model Predictive Control (FCS-MPC). The proposed strategies simultaneously provide: controlled load power distribution, circulating current suppression and a high-quality load voltage waveform. A new dynamic converters deactivation mechanism is proposed. This new technique provides improved overall system efficiency and reduced power switches stress. In this paper, two multilevel based UPS systems are parallel-connected. Each UPS contains two three-level Neutral Point-Clamped-Converters (3LNPC) and a three-level DC-DC converter. The presented experimental results demonstrate the effectiveness of the proposed control strategies in several operating conditions.

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