Robust Fuzzy Sliding Mode Controller for Discrete Nonlinear Systems

In this work we are interested to discrete robust fuzzy sliding mode control. The discrete SISO nonlinear uncertain system is presented by the Takgi- Sugeno type fuzzy model state. We recall the principle of the sliding mode control theory then we combine the fuzzy systems with the sliding mode control technique to compute at each sampling time the control law. The control law comports two terms: equivalent control law and switching control law which has a high frequency. The uncertainty is replaced by its upper bound. Inverted pendulum and mass spring dumper are used to check performance of the proposed fuzzy robust sliding mode control scheme

Insights into Ionizing-Radiation-Resistant Bacteria S-Layer Proteins and Nanobiotechnology for Bioremediation of Hazardous and Radioactive Waste

S-layers are crystalline arrays formed by proteinaceous subunits that cover the outer surface of many different kinds of microorganisms. This “proteinaceous cover” is particularly important in the case of ionizing-radiation-resistant bacteria (IRRB) that might be used in bioremediating hazardous and radioactive wastes (HRW). Despite the exponential growth in the number of comparative studies and solved proteic crystal structures, the proteic networks, diversity, and bioremediation-useful structural properties of IRRB S-layers remain unknown. Here, aided by literature, a tentative model of Deinococcus radiodurans R1 S-layer proteins (SLPs) and the network of its main constituents were proposed. The domain analysis of this network was performed. Moreover, to show the diversity of IRRB S-layers, comparative genomics and computer modeling experiments were carried out. In addition, using in silico modeling, assisted by previously published data, the outermost exposed segments of D. radiodurans SlpA (surface layer protein A) that were predicted to interact with uranium were mapped. The combination of data and results pointed to various prospective applications of IRRB S-layers in nanobiotechnology for bioremediation of radioactive waste

Insights into the mechanisms governing P01 scorpion toxin effect against U87 glioblastoma cells oncogenesis

The emerging concept of small conductance Ca2+-activated potassium channels (SKCa) as pharmacological target for cancer treatment has significantly increased in recent years. In this study, we isolated the P01 toxin from Androctonus australis (Aa) scorpion venom and investigated its effect on biological properties of glioblastoma U87, breast MDA-MB231 and colon adenocarcinoma LS174 cancer cell lines. Our results showed that P01 was active only on U87 glioblastoma cells. It inhibited their proliferation, adhesion and migration with IC50 values in the micromolar range. We have also shown that P01 reduced the amplitude of the currents recorded in HEK293 cells expressing SK2 channels with an IC50 value of 3 pM, while it had no effect on those expressing SK3 channels. The investigation of the SKCa channels expression pattern showed that SK2 transcripts were expressed differently in the three cancer cell lines. Particularly, we highlighted the presence of SK2 isoforms in U87 cells, which could explain and rely on the specific activity of P01 on this cell line. These experimental data highlighted the usefulness of scorpion peptides to decipher the role of SKCa channels in the tumorigenesis process, and develop potential therapeutic molecules targeting glioblastoma with high selectivity

Backstepping Controller for Electrically Driven Flexible Joint Manipulator Under Uncertainties

The grown complexity of the robot manipulators dynamics taking into account the jointflexibility, parameter uncertainties and unknown bounded disturbances makes conventionalcontrol strategies difficult and complex to synthesize. This paper focuses on the investiga-tion into backstepping control of flexible joint manipulator driving by Brushless DC Motor(BDCM) in the presence of parameter uncertainties and unknown bounded disturbances fortracking trajectory. The goal of this paper is to compensate all uncertainties and distur-bances for flexible joint manipulator. To study the effectiveness of the controllers, backstep-ping controller has been developed for position control and an hysteresis controller has beentreated for current control. Simulation results of the response of the flexible joint manipu-lators associated with their controllers have been presented. The high performances of thebackstepping control are examined in terms of tracking accuracy and error reduction

Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network

Various methods have been proposed to provide the protection necessitated by the high voltage direct current system. In this field, most of the research is confined to various types of DC and AC line faults and a maximum of two switching converter faults. The main contribution of this study is to use a new method for fault detection in HVDC systems, using the gray wolf optimization method along with artificial neural networks. Under this method, with the help of faulted and non-faulted signals, the features of the voltage and current signals are extracted in a much shorter period of the signal. Subsequently, differences are detected with the help of an artificial neural network. In the studied HVDC system, the behavior of the rectifier, along with its controllers and the required filters are completely modeled. In this study, other methods, such as artificial neural network, radial basis function, learning vector quantization, and self-organizing map, were tested and compared with the proposed method. To demonstrate the performance of the proposed method the accuracy, sensitivity, precision, Jaccard, and F1 score were calculated and obtained as 99.00%, 99.24%, 98.74%, 98.00%, and 98.99%, respectively. Finally, according to the simulation results, it became evident that this method could be a suitable method for fault detection in HVDC systems

Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network

Various methods have been proposed to provide the protection necessitated by the high voltage direct current system. In this field, most of the research is confined to various types of DC and AC line faults and a maximum of two switching converter faults. The main contribution of this study is to use a new method for fault detection in HVDC systems, using the gray wolf optimization method along with artificial neural networks. Under this method, with the help of faulted and non-faulted signals, the features of the voltage and current signals are extracted in a much shorter period of the signal. Subsequently, differences are detected with the help of an artificial neural network. In the studied HVDC system, the behavior of the rectifier, along with its controllers and the required filters are completely modeled. In this study, other methods, such as artificial neural network, radial basis function, learning vector quantization, and self-organizing map, were tested and compared with the proposed method. To demonstrate the performance of the proposed method the accuracy, sensitivity, precision, Jaccard, and F1 score were calculated and obtained as 99.00%, 99.24%, 98.74%, 98.00%, and 98.99%, respectively. Finally, according to the simulation results, it became evident that this method could be a suitable method for fault detection in HVDC systems

Fuzzy-based MPPT algorithm implementation on FPGA chip for multi-channel photovoltaic system

Microprocessors and microcontrollers are mostly used to control electrical systems. These chips front into problems while monitoring systems that need heavy computing and important processing. Likewise, they fail while handling inputs and outputs speeds, especially with multi-channel photovoltaic (PV) systems. In comparison to a digital signal processor (DSP) and microcontroller implementations, field programmable gate array (FPGA) device is able to integrate a great number of PV channels and to achieve short development time, cost less and more flexible operation. As well, new control algorithms are increasingly complex; using new performing technologies is very motivating. Mainly, FPGA technology is adopted thanks to its ability to control complex applications and intelligent laws. In opposition to traditional controls, fuzzy logic based control presents more efficiency and reliability response for non-linear systems. Therefore, this paper deals with the execution of the fuzzy-based maximum power point tracking (MPPT) technique by the means of the FPGA chip for a multi?channel photovoltaic system. A multi-channel photovoltaic system is designed. Then, the FPGA circuit is investigated to get benefits from this hardware solution. Since software implementation way integrates a limited number of PV panels, hardware implementation is a promising solution that reduces execution time and therefore controls a huge number of photovoltaic channels. Finally, results of simulation of the fuzzy technique implementation on FPGA chip show that the proposed PV system controls more than 4400 channels. Therefore, the system output power is increased and the system profitability is improved

Design and Performance Analysis of Different Resonance Passive-Damping Solutions for LCL filter-based Grid-Interfaced DG Inverter

International audienc

Comparative study of three types of controllers for DFIG in wind energy conversion system

Abstract This paper presents an enhanced control strategy for Wind Energy Conversion System (WECS) using Doubly-Fed Induction Generator (DFIG). A robust Super-Twisting (STW) sliding mode control for variable speed wind turbine is developed to produce the optimal aerodynamic torque and improve the dynamic performance of the WECS. The electromagnetic torque of the DFIG is directly tracked using the proposed control to achieve maximum power extraction. The performance and the effectiveness of the STW control strategy are compared to conventional Sliding Mode (SM) and Proportional-Integral (PI) controllers. The proposed STW algorithm shows interesting features in terms of chattering reduction, finite convergence time and robustness against parameters variations and system disturbances

Hemitoxin, the first potassium channel toxin from the venom of the Iranian scorpion Hemiscorpius lepturus.

International audienceHemitoxin (HTX) is a new K+ channel blocker isolated from the venom of the Iranian scorpion Hemiscorpius lepturus. It represents only 0.1% of the venom proteins, and displaces [125 I]alpha-dendrotoxin from its site on rat brain synaptosomes with an IC50 value of 16 nm. The amino acid sequence of HTX shows that it is a 35-mer basic peptide with eight cysteine residues, sharing 29-69% sequence identity with other K+ channel toxins, especially with those of the alphaKTX6 family. A homology-based molecular model generated for HTX shows the characteristic alpha/beta-scaffold of scorpion toxins. The pairing of its disulfide bridges, deduced from MS of trypsin-digested peptide, is similar to that of classical four disulfide bridged scorpion toxins (Cys1-Cys5, Cys2-Cys6, Cys3-Cys7 and Cys4-Cys8). Although it shows the highest sequence similarity with maurotoxin, HTX displays different affinities for Kv1 channel subtypes. It blocks rat Kv1.1, Kv1.2 and Kv1.3 channels expressed in Xenopus oocytes with IC50 values of 13, 16 and 2 nM, respectively. As previous studies have shown the critical role played by the beta-sheet in Kv1.3 blockers, we suggest that Arg231 is also important for Kv1.3 versus Kv1.2 HTX positive discrimination. This article gives information on the structure-function relationships of Kv1.2 and Kv1.3 inhibitors targeting developing peptidic inhibitors for the rational design of new toxins targeting given K+ channels with high selectivity