Multisampled model predictive control of inverter systems: A solution to obtain high dynamic performance and low distortion

2012 ◽  
Author(s):  
Sebastien Mariethoz ◽  
Manfred Morari
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Performance ◽  
High Dynamic ◽  
Low Distortion

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 A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method

2019 ◽  
Vol 9 (24) ◽  
pp. 5413 ◽  
Author(s):  
Mingyu Lei ◽  
Ying Zhang ◽  
Lexuan Meng ◽  
Yibo Wang ◽  
Zilong Yang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Current Control ◽  
Three Phase ◽  
Modification Method ◽  
Adaptive Model Predictive Control ◽  
Three Phase Inverter ◽  
Control Accuracy

This paper proposes a novel current control method based on Model Predictive Control (MPC) for three-phase inverters. The proposed method is based on an Adaptive MPC (A-MPC) with a PWM modulation. An innovative model parameter estimation and modification method is also proposed, leading to enhanced control accuracy. Comparing with traditional current control methods, such as PI and PR control, the proposed method has better dynamic performance. The transient dynamics, i.e., recovery time and overshoot, have been considerably improved. Simulation and experimental results are presented to validate the effectiveness of the proposal.

VARIANCE CONSTRAINED MODEL PREDICTIVE CONTROL AND APPLICATION IN LIFE EXTENDING CONTROL

2005 ◽  
Vol 29 (2) ◽  
pp. 297-314 ◽  
Author(s):  
Donglin Li ◽  
Tongwen Chen ◽  
Horacio J. Marquez ◽  
R. Kent Gooden
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Performance ◽  
Linear Matrix ◽  
Structural Durability ◽  
Damage Theory ◽  
Constrained Model ◽  
Constrained Model Predictive Control ◽  
Good Trade ◽  
Life Extending Control

The objective of life extending control (LEC), also known as damage mitigating control, is to design a controller to achieve a good trade-off between structural durability and dynamic performance in a system. In this paper, continuum fatigue damage theory for a boiler-turbine system is discussed. To reduce the accumulated damage, a variance constrained model predictive control (VCMPC) problem is developed and an algorithm via linear matrix inequalities (LMIs) is derived. This algorithm is simpler than previous ones. The controller obtained by this algorithm can assign the resultant closed-loop poles in a prescribed region. Finally, we apply the algorithm in a boiler-turbine system.

scholarly journals Robust Two-Layer Model Predictive Control for Full-Bridge NPC Inverter-Based Class-D Voltage Mode Amplifier

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1346 ◽  
Author(s):  
Xinwei Wei ◽  
Hongliang Wang ◽  
Kangliang Wang ◽  
Kui Li ◽  
Minying Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Performance ◽  
Tracking Error ◽  
Computational Effort ◽  
Multiple Control ◽  
Lumped Model ◽  
Voltage Mode ◽  
Class D ◽  
Lc Filter

Finite control set model predictive control (FCS-MPC) is able to handle multiple control objectives and constraints simultaneously with good dynamic performance. However, its industrial application is limited by its high dependence on system model and the huge computational effort. In this paper, a novel robust two-layer MPC (RM-MPC) with strong robustness is proposed for the full-bridge neutral-point clamped (NPC) voltage mode Class-D amplifier (CDA) aiming at this problem. The errors caused by the parameter mismatches or uncertainties of the LC filter and the load current are regarded as lumped disturbance and estimated by the designed Luenberger observer. The robust control can be achieved by compensating the estimated disturbance to the used predictive model. In order to reduce computation of the controller, a two-layer MPC is proposed for the full-bridge NPC inverter with an LC filter. The first layer is used to calculate the optimal output level which minimizes the tracking error of the output voltage. The second layer is used to determine the switching state for the purpose of capacitor voltage balancing. The experimental results show that the lumped model error is observed centrally through only one observer with low complexity. The two-layer MPC further reduced the computation without affecting the dynamic performance.

scholarly journals Improved Indirect Model Predictive Control for Enhancing Dynamic Performance of Modular Multilevel Converter

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1405
Author(s):  
Minh Hoang Nguyen ◽  
Sangshin Kwak
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Transient State ◽  
Computational Time ◽  
Modular Multilevel Converter ◽  
Multilevel Converter ◽  
Computational Burden

Model predictive control has become a tremendously popular control method for power converters, notably a modular multilevel converter, owing to the ability to control various objectives at once with a particular cost function and prominent dynamic performance. However, the high number of submodules in cascaded control means that the model predictive control for the modular multilevel converter suffers from a computational burden. Several approaches focused on reducing the computational burden based on limiting the number of possible switching states (possible choices) to be evaluated at each sampling instant. The dynamic performance of the modular multilevel converter is degraded in a transient state, despite the reduced computational burden. This paper presents an improved indirect model predictive control method to reduce the computational burden and enhance the dynamic performance. The proposed approach considers the steady-state and transient state individually and applies a different range of choices for each specific case. The range of choices during the steady-state is limited in order to reduce the computational burden without deteriorating the output quality, whereas the number of choices will be increased during the transient state to guarantee dynamic performance. The results that were obtained by implementing an experiment on a laboratory setup of a single-phase modular multilevel converter are presented in order to verify the proposed approach’s effectiveness. From the experimental setup, the computational time in the proposed approach was reduced by about 75% when compared with the conventional indirect model predictive control, whereas keeping fast dynamic performance.

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