İhsan Uluocak^1,*, Hakan Yavuz²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 221-234, 2023, DOI:10.32604/csse.2023.025426

Abstract The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an More >

Anuchit Aurairat, Boonyang Plangklang^*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2375-2395, 2022, DOI:10.32604/cmc.2022.025122

Abstract Nowadays, the single state inverter for the grid-connected photovoltaic (PV) systems is becoming more and more popular as they can reduce circuit complexity resulting in less power losses of the inverter. This paper focuses on the use of model predictive control (MPC) to control a 3-phase and 2-level single-state grid-connected inverter in order to regulate the PV maximum power point (MPP). The algorithm of MPC scheme was done to measure the simultaneous current signal including predicting the next sampling current flow. The reference current (I_d*) was used to control the distribution of electrical power from the… More >

Ibrahim Atawi¹, Sherif Zaid^1,2,3,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 449-469, 2022, DOI:10.32604/iasc.2022.019959

Abstract Transformerless inverters have become an important integration of the modern photovoltaic (PV) grid-tied systems. Unfortunately, it has a general safety problem regarding the earth leakage current that must be less than the recommended standards. Lately, the H7 transformerless inverter, which is a three-phase inverter with an additional switch on the DC side, is introduced to mitigate the earth leakage current. Different modulation techniques and controllers are proposed to optimize its performance. This paper proposed the application of model predictive control (MPC) to grid-connected H7 transformerless inverter supplied by the PV power system. In modeling the… More >

Xiaojuan Lu, Qingbo Zhang^*

Energy Engineering, Vol.118, No.6, pp. 1729-1740, 2021, DOI:10.32604/EE.2021.014938

Abstract In the process of grid-connected photovoltaic power generation, there are high requirements for the quality of the power that the inverter breaks into the grid. In this work, to improve the power quality of the grid-connected inverter into the grid, and the output of the system can meet the grid-connected requirements more quickly and accurately, we exhibit an approach toward establishing a mixed logical dynamical (MLD) model where logic variables were introduced to switch dynamics of the single-phase photovoltaic inverters. Besides, based on the model, our recent efforts in studying the finite control set model More >

Buddhadeva Sahoo^1,*, Sangram Keshari Routray², Pravat Kumar Rout², Mohammed M. Alhaider³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1851-1875, 2021, DOI:10.32604/cmc.2021.018535

Abstract This paper suggests a combined novel control strategy for DFIG based wind power systems (WPS) under both nonlinear and unbalanced load conditions. The combined control approach is designed by coordinating the machine side converter (MSC) and the load side converter (LSC) control approaches. The proposed MSC control approach is designed by using a model predictive control (MPC) approach to generate appropriate real and reactive power. The MSC controller selects an appropriate rotor voltage vector by using a minimized optimization cost function for the converter operation. It shows its superiority by eliminating the requirement of transformation,… More >

Xiaokan Wang^1,2,*, Qiong Wang¹, Liang Shuang³, Chao Chen⁴

Computer Systems Science and Engineering, Vol.39, No.2, pp. 237-250, 2021, DOI:10.32604/csse.2021.015945

Abstract Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard More >

Xiaojuan Lu, Zeping Liang^*

Energy Engineering, Vol.118, No.4, pp. 1171-1183, 2021, DOI:10.32604/EE.2021.014724

Abstract There are two prominent features in the process of temperature control in solar collector field. Firstly, the dynamic model of solar collector field is nonlinear and complex, which needs to be simplified. Secondly, there are a lot of random and uncontrollable, measurable and unmeasurable disturbances in solar collector field. This paper uses Taylor formula and difference approximation method to design a dynamic matrix predictive control (DMC) by linearizing and discretizing the dynamic model of the solar collector field. In addition, the purpose of controlling the stability of the outlet solar field salt temperature is achieved More >

Xiaokan Wang^*, Qiong Wang

Journal on Internet of Things, Vol.2, No.2, pp. 81-87, 2020, DOI:10.32604/jiot.2020.010225

Abstract A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle, but also effectively save fuel and reduce emissions. In this paper, the construction of model predictive control in hybrid electric vehicle is proposed. The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm. The simulation of hybrid electric vehicle is carried out under a specific working condition. The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably More >

Dehua Shi^1,4, Shaohua Wang^1,2,*, Yingfeng Cai¹, Long Chen¹, ChaoChun Yuan¹, ChunFang Yin³

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 27-39, 2020, DOI:10.31209/2018.100000062

Abstract Model predictive control (MPC), owing to the capability of dealing with nonlinear and constrained problems, is quite promising for optimization. Different MPC strategies are investigated to optimize HEV nonlinear energy management for better fuel economy. Based on Bellman’s principle, dynamic programming is firstly used in the limited horizon to obtain optimal solutions. By considering MPC as a nonlinear programming problem, sequential quadratic programming (SQP) is used to obtain the descent directions of control variables and the current control input is further derived. To reduce computation and meet the requirements of real-time control, the nonlinear model More >

Xiaokan Wang^{1, 2, *}, Qiong Wang², Shuang Liang³

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1859-1867, 2020, DOI:10.32604/cmc.2020.011032

Abstract Urban rail transit has the advantages of large traffic capacity, high punctuality and zero congestion, and it plays an increasingly important role in modern urban life. Braking system is an important system of urban rail train, which directly affects the performance and safety of train operation and impacts passenger comfort. The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity. Also, urban rail transit has the characteristics of high speed, short station distance, frequent starting, and frequent braking. This makes the braking control system constitute a… More >