Improved model predictive control for three-phase inverter with output LC filter

Mohamed, Ihab S.; Zaid, Sherif A.; Abu-Elyazeed, Mohamed F.; ElSayed, Hany M.

doi:10.1504/ijmic.2015.070642

Cited by 26 publications

(30 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where a = e j(2π/3) . The switching devices are assumed to be ideal switches, therefore the process of switching-ON/-OFF is not taken into consideration [28]. Fig.…”

Section: A System Description Via Clarke Transformationmentioning

confidence: 99%

“…3) The proposed strategy exhibits very low computational cost compared to [34], [35], with much faster dynamic performance and significantly improved steady-state performance compared to conventional methods.…”

Section: Introductionmentioning

confidence: 97%

“…With the aim of getting lower THD and improving steady and dynamic performance, many methods have been proposed in the literature [29], [33]. For instance, the deployment of longer prediction horizons is presented in [34]. However, this results in a significant increase in computational cost.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

et al. 2019

Self Cite

View full text Add to dashboard Cite

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANNbased control strategy.Index Terms-Three-phase inverter, model predictive control, artificial neural network, UPS systems.

show abstract

“…where a = e j(2π/3) . The switching devices are assumed to be ideal switches, therefore the process of switching-ON/-OFF is not taken into consideration [28]. Fig.…”

Section: A System Description Via Clarke Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…MPC, also called receding horizon control or moving horizon control is an advanced control technique of automatic (Mohamed et al, 2015). It has proven a very important progress in the research field as well as industrial applications.…”

Section: Predictive Control Using Mld Approachmentioning

confidence: 99%

Modelling and predictive control of an inverted pendulum system by MLD approach: multivariable case

Saidi

Hammi

Douik

2017

IJMIC

View full text Add to dashboard Cite

This paper deals with a class of hybrid systems that is presented by mixed logical dynamical (MLD) formalism. This approach is well-adapted to hybrid systems, particularly because of its ability not only to model systems involving continuous and discrete dynamics under constraints, but also to address several issues such as model predictive control (MPC) of this class of systems. This approach will be illustrated by the multivariable modelling of the inverted pendulum system. Therefore, the resultant system is well-posed for MPC strategy. Simulations were effected using the HYSDEL compiler to illustrate the efficiency of this formalism.

show abstract

“…Based on the threshold set by controller configuration, this method will neglect the input of smaller singular value in the corresponding direction, and thus avoid input that may possibly lead to the instability (Cai et al, 2010). AspenTech's advanced control software DMCplus adopted input move suppression (IMS) control strategy to solve the model ill-conditioned problem (Mohamed et al, 2015;Huo et al, 2013). This method decreased the inputs action by directly increasing the values of diagonal elements in LS problem and decreasing the condition number.…”

Section: Introductionmentioning

confidence: 99%

Analysis and correction of ill-conditioned model in multivariable model predictive control

Peng

Zou

et al. 2016

IJMIC

View full text Add to dashboard Cite

Ill-conditioned model usually appears in modelling high purity and complicated production processes such as the high-purity distillation column. Model predictive control (MPC) via closed-loop feedback is a class of methods for which a plant model is used to forecast the outputs or states of controlled systems and to then solve a linear or nonlinear programming with multivariable and constraints by so-called receding horizon optimisation. Ill-conditions can cause many negative effects on the implementation of MPC including system instability and controller failure. In this paper, these phenomena such as output static error and stability decreasing of ill-conditioned model caused in MPC are found by simulating simple examples, and the close correlation between the movement direction of the controlled system output and the characteristics of ill-conditioned model is also observed. The geometry tools and singular value decomposition (SVD) in linear algebra are used to analyse the essential cause of ill-conditioned model generation. A new offline quantitative strategy is proposed to improve the ill-conditioned model. Based on modified model of reengineering and implementation of model predictive control, the closed-loop control performance and stability can be significantly enhanced.

show abstract

Improved model predictive control for three-phase inverter with output LC filter

Cited by 26 publications

References 28 publications

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

Modelling and predictive control of an inverted pendulum system by MLD approach: multivariable case

Analysis and correction of ill-conditioned model in multivariable model predictive control

Contact Info

Product

Resources

About