Improved modulated model-predictive control for PMSM drives with reduced computational burden

In recent years, modular multilevel converters (MMCs) have developed rapidly, and are widely used in medium and high voltage applications. Model predictive control (MPC) has attracted wide attention recently, and its advantages include straightforward implementation, fast dynamic response, simple system design, and easy handling of multiple objectives. The main technical challenge of the conventional MPC for MMC is the reduction of computational complexity of the cost function without the reduction of control performance of the system. Some modified MPC scan decrease the computational complexity by evaluating the number of on-state sub-modules (SMs) rather than the number of switching states. However, the computational complexity is still too high for an MMC with a huge number of SMs. A reverse MPC (R-MPC) strategy for MMC was proposed in this paper to further reduce the computational burden by calculating the number of inserted SMs directly, based on the reverse prediction of arm voltages. Thus, the computational burden was independent of the number of SMs in the arm. The control performance of the proposed R-MPC strategy was validated by Matlab/Simulink software and a down-scaled experimental prototype.

Download Full-text

Lyapunov model predictive control to optimise computational burden, reference tracking and THD of three‐phase four‐leg inverter

IET Power Electronics ◽

10.1049/iet-pel.2018.5732 ◽

2019 ◽

Vol 12 (5) ◽

pp. 1061-1070 ◽

Cited By ~ 3

Author(s):

Abdul Mannan Dadu ◽

Saad Mekhilef ◽

Tey Kok Soon

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Reference Tracking ◽

Computational Burden ◽

Three Phase

Download Full-text

Sequential Model Predictive Control of Three-Phase Direct Matrix Converter

Energies ◽

10.3390/en12020214 ◽

2019 ◽

Vol 12 (2) ◽

pp. 214 ◽

Cited By ~ 5

Author(s):

Jianwei Zhang ◽

Margarita Norambuena ◽

Li Li ◽

David Dorrell ◽

Jose Rodriguez

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Weighting Factor ◽

Matrix Converter ◽

Cost Functions ◽

Computational Burden ◽

Three Phase ◽

The Matrix ◽

Factor Design

The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required. Compared with the standard MPC, the computation burden is reduced because only the pre-selected switch states are evaluated in the second and subsequent sequential cost functions. In addition, the prediction model computation for the following cost functions is also reduced. Specifying the priority for control objectives can be achieved. A comparative study with traditional MPC is carried out both in simulation and an experiment. Comparable control performance to the traditional MPC is achieved. This controller is suitable for the MC because of the reduced computational burden. Simulation and experimental results verify the effectiveness of the proposed strategy.

Download Full-text

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

International Journal of Intelligent Systems ◽

10.1002/int.22576 ◽

2021 ◽

Author(s):

M. Elsisi ◽

Mohamed A. Ebrahim

Keyword(s):

Optimal Design ◽

Model Predictive Control ◽

Predictive Control ◽

Autonomous Vehicle ◽

Computational Burden

Download Full-text

Model predictive control for linear induction machines with less computational burden

2016 IEEE Conference on Electromagnetic Field Computation (CEFC) ◽

10.1109/cefc.2016.7816303 ◽

2016 ◽

Author(s):

Jianqiao Zou ◽

Wei Xu ◽

Caiyong Ye

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Induction Machines ◽

Linear Induction ◽

Computational Burden ◽

Linear Induction Machines

Download Full-text

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

Electronics ◽

10.3390/electronics9091405 ◽

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.

Download Full-text

A Low-Complexity Modulated Model Predictive Current Control Scheme for PMSM based Starter Generator Control System in More Electric Aircraft

10.36227/techrxiv.12815216.v1 ◽

2020 ◽

Author(s):

Qi Wang ◽

Haitao Yu ◽

Chen Li ◽

Seang Shen Yeoh ◽

Xiaoyu Lang ◽

...

Keyword(s):

Control System ◽

Model Predictive Control ◽

Predictive Control ◽

Low Complexity ◽

Current Control ◽

Switching Frequency ◽

Computational Burden ◽

Electric Aircraft ◽

Starter Generator ◽

More Electric Aircraft

Modulated model predictive control (M2PC) has recently emerged as a possible solution for control in starter generator systems in the more electric aircraft (MEA), due to its advantages of fixed switching frequency, fast response and good performance. However, conventional M2PC requires the prediction of each possible output voltage vector, which involves a heavy computational burden for the processor, especially for multilevel converters. This is an obstacle for practical industrial applications. To solve this problem this paper introduces a new, low-complexity modulated model predictive control (LC-M2PC) for a starter generator control system with a neutral point clamped (NPC) converter. The proposed LC-M2PC only needs prediction action once in each control interval, which can reduce the computational burden of processor. Fixed switching frequency is maintained and it can achieve a lower total harmonic distortion (THD) current than conventional M2PC, using space vector modulation (SVM). This proposed LC-M2PC method is validated on a prototype electrical starter generator (ESG) system test rig with three-level NPC converter. Experimental results verify the effectiveness of the proposed method.<br>

Download Full-text

Vehicle Path-Tracking Linear-Time-Varying Model Predictive Control Controller Parameter Selection Considering Central Process Unit Computational Load

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4042196 ◽

2019 ◽

Vol 141 (5) ◽

Cited By ~ 6

Author(s):

Zejiang Wang ◽

Yunhao Bai ◽

Junmin Wang ◽

Xiaorui Wang

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Performance Optimization ◽

Linear Time ◽

Parameter Tuning ◽

Parameter Selection ◽

Process Unit ◽

Central Process ◽

Computational Burden ◽

Controller Performance

Model predictive control (MPC) has drawn a considerable amount of attention in automotive applications during the last decade, partially due to its systematic capacity of treating system constraints. Even though having received broad acknowledgements, there still exist two intrinsic shortcomings on this optimization-based control strategy, namely the extensive online calculation burden and the complex tuning process, which hinder MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed. Nevertheless, the majority of these approaches treat these two issues independently. However, parameter tuning in fact has double-sided effects on both the controller performance and the real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization, and computational burden easement, a look-up table-based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. matlab-carsim conjoint simulations show the effectiveness of the proposed strategy.

Download Full-text