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 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 An Improved Model-Free Current Predictive Control Method for SPMSM Drives

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 134672-134681
Author(s):  
Xuerong Li ◽  
Yang Wang ◽  
Xingzhong Guo ◽  
Xing Cui ◽  
Shuo Zhang ◽  
...  
Keyword(s):  
Predictive Control ◽  
Control Method ◽  
Model Free ◽  
Free Current ◽  
Improved Model

Adaptive Neural Network Based Predictive Control of Nonlinear Systems With Slow Dynamics

2020 ◽  
Author(s):  
Mark Spiller ◽  
Fateme Bakhshande ◽  
Dirk Söffker
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Predictive Control ◽  
Mimo System ◽  
Prediction Equation ◽  
Control Input ◽  
System Behavior ◽  
Slow Dynamics ◽  
Model Free ◽  
Data Driven Approach

Abstract In this paper a data-driven approach for model-free control of nonlinear systems with slow dynamics is proposed. The system behavior is described using a local model respectively a neural network. The network is updated online based on a Kalman filter. By predicting the system behavior two control approaches are discussed. One is obtained by calculating a control input from the one step ahead prediction equation using least squares, the other is obtained by solving a standard linear model predictive control problem. The approaches are tested on a constrained nonlinear MIMO system with slow dynamics.

scholarly journals A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianfeng Yang ◽  
Yang Liu ◽  
Rui Yan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Parameter Uncertainty ◽  
Prediction Error ◽  
Continuous Control ◽  
Power Electronic ◽  
Parameter Mismatch ◽  
Three Phase ◽  
Finite Control ◽  
Control Set

Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny.

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.

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