Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental settin

Optimal control design for robust fuzzy friction compensation in a robot joint

This paper presents a methodology for the compensation of nonlinear friction in a robot joint structure based on a fuzzy local modeling technique. To enhance the tracking performance of the robot joint, a dynamic model is derived from the local physical properties of friction. The model is the basis of a precompensator taking into account the dynamics of the overall corrected system by means of a minor loop. The proposed structure does not claim to faithfully reproduce complex phenomena driven by friction. However, the linearity of the local models simplifies the design and implementation of the observer, and its estimation capabilities are improved by the nonlinear integral gain. The controller can then be robustly synthesized using linear matrix inequalities to cancel the effects of inexact friction compensation. Experimental tests conducted on a robot joint with a high level of friction demonstrate the effectiveness of the proposed fuzzy observer-based control strategy for tracking system trajectories when operating in zero-velocity regions and during motion reversals

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients

An Integrated Physiological Model of the Lung Mechanics and Gas Exchange Using Electrical Impedance Tomography in the Analysis of Ventilation Strategies in ARDS Patients

Mouloud Denai, M. Mahfouf, A. Wang, D. A. Linkens, and G. H. Mills, 'An Integrated Physiological Model of the Lung Mechanics and Gas Exchange Using Electrical Impedance Tomography in the Analysis of Ventilation Strategies in ARDS Patients'. Paper presented at the 3rd International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2010), 20 - 23 January 2010, Valencia, Spain.Peer reviewedFinal Published versio

Application of a Novel Synergetic Control for Optimal Power Extraction of a Small-Scale Wind Generation System with Variable Loads and Wind Speeds

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The synergetic control technique (SCT) has the solution for understanding the symmetry inherent in the non-linear properties of wind turbines (WTs); therefore, they achieve excellent performance and enhance the operation of the WT. Small-scale WTs are efficient and cost-effective; they are usually installed close to where the generated electricity is used. This technology is gaining popularity worldwide for off-grid electricity generation, such as in rural homes, farms, small factories, and commercial properties. To enhance the efficiency of the WT, it is vital to operate the WT at its maximum power. This work proposes an efficient and fast maximum power point tracking (MPPT) technique based on the SCT to eradicate the drawbacks of the conventional methods and enhance the operation of the WT at the MPP regardless of wind speed and load changes. The SCT has advantages, such as robustness, simplified design, fast response, no requirement for knowledge of WT characteristics, no need for wind sensors or intricate power electronics, and straightforward implementation. Furthermore, it improves speed convergence with minimal steady-state oscillations at the MPP. The investigated configuration involves a wind-driven permanent magnet synchronous generator (PMSG), uncontrolled rectifier, boost converter, and variable load. The two converters are used to integrate the PMSG with the load. Three scenarios (step changes in wind speed, stochastic changes in wind speed, and variable electrical load) are studied to assess the SCT. The results prove a high performance of the suggested MPPT control method for a fast convergence speed, boosted WT efficacy, low oscillation levels, and applicability under a variety of environmental situations. This work used the MATLAB/Simulink program and was then implemented on a dSPACE 1104 control board to assess the efficacy of the SCT. Furthermore, experimental validation on a 1 kW Darrieus-type WT driving a PMSG was performed.Peer reviewe

Epithelial to mesenchymal transition is associated with rapamycin resistance

Rapamycin analogues have antitumor efficacy in several tumor types, however few patients demonstrate tumor regression. Thus, there is a pressing need for markers of intrinsic response/resistance and rational combination therapies. We hypothesized that epithelial-to-mesenchymal transition (EMT) confers rapamycin resistance. We found that the epithelial marker E-cadherin protein is higher in rapamycin sensitive (RS) cells and mesenchymal breast cancer cell lines selected by transcriptional EMT signatures are less sensitive to rapamycin. MCF7 cells, transfected with constitutively active mutant Snail, had increased rapamycin resistance (RR) compared to cells transfected with wild-type Snail. Conversely, we transfected two RR mesenchymal cell lines—ACHN and MDA-MB-231—with miR-200b/c or ZEB1 siRNA to promote mesenchymal-to-epithelial transition. This induced E-cadherin expression in both cell lines, and ACHN demonstrated a significant increase in RS. Treatment of ACHN and MDA-MB-231 with trametinib modulated EMT in ACHN cells in vitro. Treatment of MDA-MB-231 and ACHN xenografts with trametinib in combination with rapamycin resulted in significant growth inhibition in both but without an apparent effect on EMT. Future studies are needed to determine whether EMT status is predictive of sensitivity to rapalogs and to determine whether combination therapy with EMT modulating agents can enhance antitumor effects of PI3K/mTOR inhibitors

High Performance Multicell Series Inverter-Fed Induction Motor Drive

This document is the Accepted Manuscript version of the following article: M. Khodja, D. Rahiel, M. B. Benabdallah, H. Merabet Boulouiha, A. Allali, A. Chaker, and M. Denai, ‘High-performance multicell series inverter-fed induction motor drive’, Electrical Engineering, Vol. 99 (3): 1121-1137, September 2017. The final publication is available at Springer via DOI: https://doi.org/10.1007/s00202-016-0472-4.The multilevel voltage-source inverter (VSI) topology of the series multicell converter developed in recent years has led to improved converter performance in terms of power density and efficiency. This converter reduces the voltage constraints between all cells, which results in a lower transmission losses, high switching frequencies and the improvement of the output voltage waveforms. This paper proposes an improved topology of the series multicell inverter which minimizes harmonics, reduces torque ripples and losses in a variable-speed induction motor drive. The flying capacitor multilevel inverter topology based on the classical and modified phase shift pulse width modulation (PSPWM, MPSPWM) techniques are applied in this paper to minimize harmonic distortion at the inverter output. Simulation results are presented for a 2-kW induction motor drive and the results obtained demonstrate reduced harmonics, improved transient responses and reference tracking performance of the voltage in the induction motor and consequently reduced torque ripplesPeer reviewe

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Power management and control strategies for off-grid hybrid power systems with renewable energies and storage

This document is the Accepted Manuscript of the following article: Belkacem Belabbas, Tayeb Allaoui, Mohamed Tadjine, and Mouloud Denai, 'Power management and control strategies for off-grid hybrid power systems with renewable energies and storage', Energy Systems, September 2017. Under embargo. Embargo end date: 19 September 2018. The final publication is available at Springer via https://doi.org/10.1007/s12667-017-0251-y.This paper presents a simulation study of standalone hybrid Distributed Generation Systems (DGS) with Battery Energy Storage System (BESS). The DGS consists of Photovoltaic (PV) panels as Renewable Power Source (RPS), a Diesel Generator (DG) for power buck-up and a BESS to accommodate the surplus of energy, which may be employed in times of poor PV generation. While off-grid DGS represent an efficient and cost-effective energy supply solution particularly to rural and remote areas, fluctuations in voltage and frequency due to load variations, weather conditions (temperature, irradiation) and transmission line short-circuits are major challenges. The paper suggests a hierarchical Power Management (PM) and controller structure to improve the reliability and efficiency of the hybrid DGS. The first layer of the overall control scheme includes a Fuzzy Logic Controller (FLC) to adjust the voltage and frequency at the Point of Common Coupling (PCC) and a Clamping Bridge Circuit (CBC) which regulates the DC bus voltage. A maximum power point tracking (MPPT) controller based on FLC is designed to extract the optimum power from the PV. The second control layer coordinates among PV, DG and BESS to ensure reliable and efficient power supply to the load. MATLAB Simulink is used to implement the overall model of the off-grid DGS and to test the performance of the proposed control scheme which is evaluated in a series of simulations scenarios. The results demonstrated the good performance of the proposed control scheme and effective coordination between the DGS for all the simulation scenarios considered.Peer reviewedFinal Accepted Versio