Hassani Messaoud

Bio: Hassani Messaoud is an academic researcher from University of Monastir. The author has contributed to research in topics: Nonlinear system & Laguerre polynomials. The author has an hindex of 18, co-authored 203 publications receiving 1188 citations. Previous affiliations of Hassani Messaoud include University of Picardie Jules Verne & École Normale Supérieure.

Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring

Abstract: This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA) for handling nonlinear dynamic systems. The proposed method is entitled Moving Window Reduced Kernel Principal Component Analysis (MW-RKPCA). It consists firstly in approximating the principal components (PCs) of the KPCA model by a reduced data set that approaches "properly" the system behavior in the order to elaborate an RKPCA model. Secondly, the proposed MW-RKPCA consists on updating the RKPCA model using a moving window. The relevance of the proposed MW-RKPCA technique is illustrated on a Tennessee Eastman process.

New fault detection method based on reduced kernel principal component analysis (RKPCA)

Abstract: This paper proposes a new method for fault detection using a reduced kernel principal component analysis (RKPCA). The proposed RKPCA method consists on approximating the retained principal components given by the KPCA method by a set of observation vectors which point to the directions of the largest variances with the retained principal components. The proposed method has been tested on a chemical reactor and the results were satisfactory.

Decomposition of an ARX model on Laguerre orthonormal bases.

Abstract: In this paper, we propose a new reduced complexity model by expanding a discrete-time ARX model on Laguerre orthonormal bases. To ensure an efficient complexity reduction, the coefficients associated to the input and the output of the ARX model are expanded on independent Laguerre bases, to develop a new black-box linear ARX-Laguerre model with filters on model input and output. The parametric complexity reduction with respect to the classical ARX model is proved theoretically. The structure and parameter identification of the ARX-Laguerre model is achieved by a new proposed approach which consists in solving an optimization problem built from the ARX model without using system input/output observations. The performances of the resulting ARX-Laguerre model and the proposed identification approach are illustrated by numerical simulations and validated on benchmark manufactured by Feedback known as Process Trainer PT326. A possible extension of the proposed model to a multivariable process is formulated.

Exponential Observer for a Class of One-Sided Lipschitz Stochastic Nonlinear Systems

Abstract: This paper investigates observer design for a class of nonlinear one-sided Lipschitz stochastic systems with multiplicative noises It is shown that the almost sure exponential convergence of the observation error could be treated by decoupling the state from this error This is done by using a new theorem dedicated to triangular stochastic systems The observer gains are designed using a polytopic technique exploiting the structure of the control inputs, coupled with a descriptor systems approach

Robust adaptive sliding mode control for uncertain systems with unknown time-varying delay input.

Abstract: This article focuses on robust adaptive sliding mode control law for uncertain discrete systems with unknown time-varying delay input, where the uncertainty is assumed unknown. The main results of this paper are divided into three phases. In the first phase, we propose a new sliding surface is derived within the Linear Matrix Inequalities (LMIs). In the second phase, using the new sliding surface, the novel Robust Sliding Mode Control (RSMC) is proposed where the upper bound of uncertainty is supposed known. Finally, the novel approach of Robust Adaptive Sliding ModeControl (RASMC) has been defined for this type of systems, where the upper limit of uncertainty which is assumed unknown. In this new approach, we have estimate the upper limit of uncertainties and we have determined the control law based on a sliding surface that will converge to zero. This novel control laws are been validated in simulation on an uncertain numerical system with good results and comparative study. This efficiency is emphasized through the application of the new controls on the two physical systems which are the process trainer PT326 and hydraulic system two tanks.

Nonlinear System Identification

Abstract: This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

A low-delay protocol for multishop wireless body area networks

Abstract: Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.

Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings

Abstract: Rolling element bearings are among the most widely used and also vulnerable components in rotating machinery equipment. Recently, prognostics and health management of rolling element bearings is more and more attractive both in academics and industry. However, many studies have been focusing on the prognostic aspect of bearing prognostics and health management and few efforts have been performed in relation to the optimal degradation feature selection issue. For more effective and efficient remaining useful life predictions, three goodness metrics of correlation, monotonicity and robustness are defined and combined for automatically more relevant degradation feature selection in this paper. Effectiveness of the proposed method is verified by rolling element bearing degradation experiments. Copyright © 2015 John Wiley & Sons, Ltd.

Dye-sensitized solar cells under ambient light powering machine learning : towards autonomous smart sensors for the internet of things

Abstract: The field of photovoltaics gives the opportunity to make our buildings "smart'' and our portable devices "independent", provided effective energy sources can be developed for use in ambient indoor ...