An enhanced control strategy based imaginary swapping instant for induction motor drives

The main aim of this paper is to present a novel control approach of an induction machine (IM) using an improved space vector modulation based direct torque control (SVM-DTC) on the basis of imaginary swapping instant technique. The improved control strategy is presented to surmount the drawbacks of the classical direct torque control (DTC) and to enhance the dynamic performance of the induction motor. This method requires neither angle identification nor sector determination; the imaginary swapping instant vector is used to fix the effective period in which the power is transferred to the IM. Both the classical DTC method and the suggested adaptive DTC techniques have been carried out in MATLAB/SimulinkTM. Simulation results shows the effectiveness of the enhanced control strategy and demonstrate that torque and flux ripples are massively diminished compared to the conventional DTC (CDTC) which gives a better performance. Finally, the system will also be tested for its robustness against variations in the IM parameters

Constrained discrete model predictive control of a greenhouse system temperature

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives

Analysis of the performance of inventory management systems using the SCOR model and Batch Deterministic and Stochastic Petri Nets

In this article, we present a contribution to modeling, evaluation, and analysis of the inventory management systems performance and more generally stochastic discrete event systems with a batch behavior. For this contribution, we combine two models: the Supply Chain Operations Reference model, proposed by the Supply Chain Council, with the Batch Deterministic and Stochastic Petri Nets, which constitutes a very powerful dynamic modeling tool. To do this, we applied these tools on a typical model of inventory management system in order to show how the combination of these two tools can help us to model and analyze the performance of the inventory management system and to provide information on their behavior and the effects of their parameters. A resolution of the stochastic process associated with the warehouse management system will allow us to calculate the following performance indicators: average stock, average cost of stock, probability of an empty stock, and average supply and frequency. These indicators will help to monitor the activity of our stock management system, and therefore make the right decisions for the development of the organization

Forecasting of demand using ARIMA model

The work presented in this article constitutes a contribution to modeling and forecasting the demand in a food company, by using time series approach. Our work demonstrates how the historical demand data could be utilized to forecast future demand and how these forecasts affect the supply chain. The historical demand information was used to develop several autoregressive integrated moving average (ARIMA) models by using Box–Jenkins time series procedure and the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 0, 1) and it was validated by another historical demand information under the same conditions. The results obtained prove that the model could be utilized to model and forecast the future demand in this food manufacturing. These results will provide to managers of this manufacturing reliable guidelines in making decisions

Constrained Discrete Model Predictive Control of a Greenhouse Relative Humidity

In this paper, we present a Constrained Discete Model Predictive Control (CDMPC) strategy application for relative humidity control. In this sense, and for our system inside humidity dynamics description, a green-house prototype is engaged and a state space form which fits properly a set of collected data of the greenhouse humidity dynamics is presented as mathematical model. This latest is used for the CDMPC starategy application, which purpose is to select the best control moves based on an optimization procedure regarding the constraints on the control. By the means of Matlab/ Simulink and Yalmip toolbox algorithms, numerical simulations were held to proove the effectiveness of the controller, garanteeing both the constraints feasibility and system stability

An hybrid control strategy design for Photovoltaic battery charger

This work presents the design and the modelling of an improved lead acid Battery charger for solar photovoltaic applications. In this context, the control unit of the battery charger is composed of two intelligent controllers. In the first state, an MPPT controller based on an Adaptive neuro-fuzzy inference system (ANFIS) is used to extract the full maximum power provided by the PV array, in the second stage, the control unit switches to the regulator mode on the basis of a fuzzy logic control block that offers the three charging stages according to DIN 41773 standard for lead-acid battery. In order to demonstrate the performance of the ANFIS controller, this paper presents also a comparison of several MPPT techniques for solar PV applications

An intelligent lead-acid battery closed-loop charger using a combined fuzzy controller for PV applications

This paper presents the modeling of an intelligent combined MPPT and Lead-Acid battery charger controller for standalone solar photovoltaic systems. It involves the control of a DC/DC buck converter through a control unit, which contains two cascaded fuzzy logic controllers (FLC), that adjusts the required duty cycle of the converter according to the state of charge and the three stage lead acid battery charging system. The first fuzzy logic controller (FLC1) consists of an MPPT controller to extract the maximum power produced by the PV array, while the second fuzzy controller (FLC2) is aimed to control the voltage across the battery to ensure the three stage charging approach. This solution of employing two distinct cascaded fuzzy controllers surmounts the drawbacks of the classical chargers in which the voltage provided to the lead acid battery is not constant owing to the effects of the MPPT control which can automatically damage the battery. Thus, the suggested control strategy has the benefit of extracting the full power against the PV array, avoiding battery damage incurred by variable MPPT voltage and increasing the battery’s lifespan

Source Apportionment and Diurnal Variability of Autumn-Time Black Carbon in a Coastal City of Salé, Morocco

This research aims to understand the temporal variation of concentrations of equivalent black carbon (eBC) and to calculate the fossil fuel (BCff) and biomass combustion (BCwb) contribution to eBC during the 2020 autumn season. In-situ measurements of eBC and NO2 were performed for this aim in Sale, Morocco. The contribution of BCff and BCwb was assigned based on the spectrum dependence of BC absorption. The average eBC concentration was 1.9 ± 2.2 µg/m3 with a contribution of 13% for BCwb. The eBC was strongly correlated with NO2 (R2 = 0.63). Fossil fuel combustion is the most significant contributor to eBC and NO2 concentrations

Application analysis of ANFIS strategy for greenhouse climate parameters prediction: Internal temperature and internal relative humidity case of study

The present paper, introduces Adaptive Neuro Fuzzy Inference System (ANFIS) as one of the most mature and intelligent methods to predicte internal temperature and relative humidity of a greenhouse system. To conduct the application of the proposed strategy, an experimenntal greenhouse equipied with several sensors and actuators is engaged. In this sense a data base was collected during a period of day time where the temperature and relative humidity dynamics were observed inpresence of others climatic parameters and the actuators’ actions. The results demonstrate that by using ANFIS method, the predictions match the target points with a good accuracy. Therefore, the effectiveness of the strategy in term of both inside climate parameters’ prediction is guaranteed