Faouzi Msahli | ENIM : National Engineering School of Monastir - TUNISIE (original) (raw)
Papers by Faouzi Msahli
Decision Making and Soft Computing, 2014
ABSTRACT Selection of relevant parameters from a high dimensional process operation setting space... more ABSTRACT Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric mechanical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between mechanical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, a panel of experts provides their ranking lists of mechanical features according to their professional knowledge. Also by applying OWA, the data sensitivity-based ranking list and the knowledge-based ranking list are combined to determine the final ranking list and the final relevant mechanical parameters for a given sensory quality feature.
Journal of Control Theory and Applications, 2013
This paper deals with the design of an output feedback predictive controller for induction motors... more This paper deals with the design of an output feedback predictive controller for induction motors. The fundamental interest of the proposed controller is the capability of decoupling the mechanical speed and the rotor fluxes, without degradation against the variation of rotor resistance and load torque. Hence, the contribution is to apply two estimation procedures in order to achieve this goal. Namely, an unknown input observer (UIO) is used for the constant time estimation whereas a heuristic solution is exploited for the load torque update. Moreover, rotor flux components are recovered as an unavailable state of the system. Effectiveness of the proposed observers and the performance of the controller are confirmed by simulation results.
Neural networks can be considered to be new modelling tools in process control and especially in ... more Neural networks can be considered to be new modelling
tools in process control and especially in non-linear dynamical
systems cases. Their ability to approximate non-linear
functions has been very often demonstrated and tested by simulation
and experimental studies. In this paper, a predictive control
strategy of a semi-batch reactor based on neural network models
is proposed. Results of a non-linear control of the reactant temperature
of a semi-batch reactor are presented. The process identification
is composed of an off-line phase that consists in training
the network, and of an on-line phase that corresponds to the
neural model adaptation so that it fits any modification of the
process dynamics. Experimental results when using this method
to control a semi-batch reactor are reported and show the great
potential of this strategy in controlling non-linear processes.
In recent years, much attention has been focused upon predictive control of nonlinear systems. Th... more In recent years, much attention has been focused upon predictive
control of nonlinear systems. The implementation of such
a control strategy for real processes has greatly improved
their performance. This paper deals with a model-based predictive
control (MBPC) strategy using a generalised Hammerstein
model and its application to the temperature control of a semibatch
reactor. Both unconstrained and constrained adaptive
control problems are considered. A simple identification method
based on the weighted recursive least squares method (WRLS)
is used to estimate the model parameters on-line. An indirect
adaptive nonlinear controller is designed by combining the
predictive controller with an indirect parameter estimation
algorithm. This adaptive scheme has been applied for the
control of a semi-batch chemical reactor. Experimental results
show that the performance of the generalised Hammerstein
MBPC (NLMBPC) was significantly better than that of a linear
model predictive controller (LMBPC).
This paper addresses the problem of performance analysis of linear algebra methods for higher-ord... more This paper addresses the problem of performance analysis of linear algebra methods for higher-order statistics based identification. We propose another approach which can be used to evaluate the performance of these methods. This approach is based on the use of the condition number of system of equations. To illustrate the effectiveness of our approach, several simulation examples are presented
In this paper, new approaches for the identification of FIR systems using HOS are proposed. The u... more In this paper, new approaches for the identification of FIR systems using HOS are proposed. The unknown model parameters are obtained using optimization algorithms. In fact, the proposed method consists first in defining an optimization problem and second in using an appropriate algorithm to resolve it. Moreover, we develop a new method for estimating the order of FIR models using only the output cumulants. The results presented in this paper illustrate the performance of our methods and compare them with a range of existing approaches.
We have presented two contributions for identification of LTI NMP FIR systems. They use the fourt... more We have presented two contributions for identification of LTI NMP FIR systems. They use the fourth order cumulants of the noisy observations of the system output and consequently yield consistent parameters estimation in the presence of additive Gaussian noise. Both recursive closed-form and batch least squares solutions of the parameters estimation are proposed for each contribution. The second contribution allows to reduce the redundancy of the vector of unknown parameters is developed. Finally, the simulation results showed that the performance of our contributions is better than the other methods.
Materials Letters, 2009
Recently, the use of lignocellulosic fibres to reinforcing composite has received an increased at... more Recently, the use of lignocellulosic fibres to reinforcing composite has received an increased attention. However, lack of good interfacial adhesion makes important the treatment of raw materials. Chemical treatment prepared the raw material to be useful by elimination of gummy and waxy substances. In this study, the Luffa fibres were treated by tow methods: alkali treatment and mixed treatment (sodium hydroxide and hydrogen peroxide). The effect of these treatments on the structure of fibres was showed using SEM and XRD (X-Ray Diffraction) analysis. The SEM results revealed that both treatments resulted in a removal of lignin, pectin and hemicellulose substances, and change the characteristics of the surface topography. The XRD analysis shows the increase of crystallinity index by many treatment conditions. We find that the alkali treatment (120°C; 3 h; 4% NaOH) shows a good cleaning and the higher crystallinity index of treated fibres. It is also interesting to note that mixed treatment can change the Luffa fibres from mat structure to fibrils structure.
This work highlights the link between the accurate knowledge of some critical parameters, such as... more This work highlights the link between the accurate knowledge of some critical parameters, such as rotor resistance and load torque, and its important role in correct speed sensorless induction motor control. Indeed, the main characteristics of the output feedback predictive controller lie in the fact that their design is made by exploiting on-line the model at each sampling instant. In the spirit to achieve high performance control, the incorporation of an observer is proposed for recovering online all machine variables (the internal states and time varying parameters). Simulation results for IMs are addressed to show the effectiveness of the control design method.
A sensorless output feedback predictive controller for induction motors requires an accurate know... more A sensorless output feedback predictive controller for induction motors requires an accurate knowledge on the model at each sampling instant, to calculate predictions of future behaviour of the process. However, some critical parameters, such as rotor resistance and load torque which are subject to large variations during operation, beside the inaccessibility of all the state, constitute major challenges for the performance of such systems. In the spirit to achieve high performance control, the incorporation of an observer is proposed for recovering online all machine variables (the internal states and time varying parameters). To this end, this work highlights the capability of the adaptive interconnected observer to simultaneous estimation of the internal states and time varying parameters, only by using the current measurements. Simulation results verify the effectiveness of the duality between control and the proposed observer to achieve high performance in the tracking objective.
International Journal of Automation and Computing, 2009
In this paper, we address the problem of structure identification of Volterra models. It consists... more In this paper, we address the problem of structure identification of Volterra models. It consists in estimating the model order and the memory length of each kernel. Two methods based on input-output crosscumulants are developed. The first one uses zero mean independent and identically distributed Gaussian input, and the second one concerns a symmetric input sequence. Simulations are performed on six models having different orders and kernel memory lengths to demonstrate the advantages of the proposed methods.
In this paper, we address the problem of structure identification of Volterra model driven by a b... more In this paper, we address the problem of structure identification of Volterra model driven by a binary symmetric independent and identically distributed sequence. The proposed method consists of estimating the model order and the memory length of each kernel. It uses crosscumulants of input-output sequence. Simulations are performed on four models having different orders and kernel memory lengths to demonstrate the advantages of the proposed method.
Decision Making and Soft Computing, 2014
ABSTRACT Selection of relevant parameters from a high dimensional process operation setting space... more ABSTRACT Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric mechanical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between mechanical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, a panel of experts provides their ranking lists of mechanical features according to their professional knowledge. Also by applying OWA, the data sensitivity-based ranking list and the knowledge-based ranking list are combined to determine the final ranking list and the final relevant mechanical parameters for a given sensory quality feature.
Journal of Control Theory and Applications, 2013
This paper deals with the design of an output feedback predictive controller for induction motors... more This paper deals with the design of an output feedback predictive controller for induction motors. The fundamental interest of the proposed controller is the capability of decoupling the mechanical speed and the rotor fluxes, without degradation against the variation of rotor resistance and load torque. Hence, the contribution is to apply two estimation procedures in order to achieve this goal. Namely, an unknown input observer (UIO) is used for the constant time estimation whereas a heuristic solution is exploited for the load torque update. Moreover, rotor flux components are recovered as an unavailable state of the system. Effectiveness of the proposed observers and the performance of the controller are confirmed by simulation results.
Neural networks can be considered to be new modelling tools in process control and especially in ... more Neural networks can be considered to be new modelling
tools in process control and especially in non-linear dynamical
systems cases. Their ability to approximate non-linear
functions has been very often demonstrated and tested by simulation
and experimental studies. In this paper, a predictive control
strategy of a semi-batch reactor based on neural network models
is proposed. Results of a non-linear control of the reactant temperature
of a semi-batch reactor are presented. The process identification
is composed of an off-line phase that consists in training
the network, and of an on-line phase that corresponds to the
neural model adaptation so that it fits any modification of the
process dynamics. Experimental results when using this method
to control a semi-batch reactor are reported and show the great
potential of this strategy in controlling non-linear processes.
In recent years, much attention has been focused upon predictive control of nonlinear systems. Th... more In recent years, much attention has been focused upon predictive
control of nonlinear systems. The implementation of such
a control strategy for real processes has greatly improved
their performance. This paper deals with a model-based predictive
control (MBPC) strategy using a generalised Hammerstein
model and its application to the temperature control of a semibatch
reactor. Both unconstrained and constrained adaptive
control problems are considered. A simple identification method
based on the weighted recursive least squares method (WRLS)
is used to estimate the model parameters on-line. An indirect
adaptive nonlinear controller is designed by combining the
predictive controller with an indirect parameter estimation
algorithm. This adaptive scheme has been applied for the
control of a semi-batch chemical reactor. Experimental results
show that the performance of the generalised Hammerstein
MBPC (NLMBPC) was significantly better than that of a linear
model predictive controller (LMBPC).
This paper addresses the problem of performance analysis of linear algebra methods for higher-ord... more This paper addresses the problem of performance analysis of linear algebra methods for higher-order statistics based identification. We propose another approach which can be used to evaluate the performance of these methods. This approach is based on the use of the condition number of system of equations. To illustrate the effectiveness of our approach, several simulation examples are presented
In this paper, new approaches for the identification of FIR systems using HOS are proposed. The u... more In this paper, new approaches for the identification of FIR systems using HOS are proposed. The unknown model parameters are obtained using optimization algorithms. In fact, the proposed method consists first in defining an optimization problem and second in using an appropriate algorithm to resolve it. Moreover, we develop a new method for estimating the order of FIR models using only the output cumulants. The results presented in this paper illustrate the performance of our methods and compare them with a range of existing approaches.
We have presented two contributions for identification of LTI NMP FIR systems. They use the fourt... more We have presented two contributions for identification of LTI NMP FIR systems. They use the fourth order cumulants of the noisy observations of the system output and consequently yield consistent parameters estimation in the presence of additive Gaussian noise. Both recursive closed-form and batch least squares solutions of the parameters estimation are proposed for each contribution. The second contribution allows to reduce the redundancy of the vector of unknown parameters is developed. Finally, the simulation results showed that the performance of our contributions is better than the other methods.
Materials Letters, 2009
Recently, the use of lignocellulosic fibres to reinforcing composite has received an increased at... more Recently, the use of lignocellulosic fibres to reinforcing composite has received an increased attention. However, lack of good interfacial adhesion makes important the treatment of raw materials. Chemical treatment prepared the raw material to be useful by elimination of gummy and waxy substances. In this study, the Luffa fibres were treated by tow methods: alkali treatment and mixed treatment (sodium hydroxide and hydrogen peroxide). The effect of these treatments on the structure of fibres was showed using SEM and XRD (X-Ray Diffraction) analysis. The SEM results revealed that both treatments resulted in a removal of lignin, pectin and hemicellulose substances, and change the characteristics of the surface topography. The XRD analysis shows the increase of crystallinity index by many treatment conditions. We find that the alkali treatment (120°C; 3 h; 4% NaOH) shows a good cleaning and the higher crystallinity index of treated fibres. It is also interesting to note that mixed treatment can change the Luffa fibres from mat structure to fibrils structure.
This work highlights the link between the accurate knowledge of some critical parameters, such as... more This work highlights the link between the accurate knowledge of some critical parameters, such as rotor resistance and load torque, and its important role in correct speed sensorless induction motor control. Indeed, the main characteristics of the output feedback predictive controller lie in the fact that their design is made by exploiting on-line the model at each sampling instant. In the spirit to achieve high performance control, the incorporation of an observer is proposed for recovering online all machine variables (the internal states and time varying parameters). Simulation results for IMs are addressed to show the effectiveness of the control design method.
A sensorless output feedback predictive controller for induction motors requires an accurate know... more A sensorless output feedback predictive controller for induction motors requires an accurate knowledge on the model at each sampling instant, to calculate predictions of future behaviour of the process. However, some critical parameters, such as rotor resistance and load torque which are subject to large variations during operation, beside the inaccessibility of all the state, constitute major challenges for the performance of such systems. In the spirit to achieve high performance control, the incorporation of an observer is proposed for recovering online all machine variables (the internal states and time varying parameters). To this end, this work highlights the capability of the adaptive interconnected observer to simultaneous estimation of the internal states and time varying parameters, only by using the current measurements. Simulation results verify the effectiveness of the duality between control and the proposed observer to achieve high performance in the tracking objective.
International Journal of Automation and Computing, 2009
In this paper, we address the problem of structure identification of Volterra models. It consists... more In this paper, we address the problem of structure identification of Volterra models. It consists in estimating the model order and the memory length of each kernel. Two methods based on input-output crosscumulants are developed. The first one uses zero mean independent and identically distributed Gaussian input, and the second one concerns a symmetric input sequence. Simulations are performed on six models having different orders and kernel memory lengths to demonstrate the advantages of the proposed methods.
In this paper, we address the problem of structure identification of Volterra model driven by a b... more In this paper, we address the problem of structure identification of Volterra model driven by a binary symmetric independent and identically distributed sequence. The proposed method consists of estimating the model order and the memory length of each kernel. It uses crosscumulants of input-output sequence. Simulations are performed on four models having different orders and kernel memory lengths to demonstrate the advantages of the proposed method.