Hassani Messaoud

In this paper, we investigate the design of a positive functional observer-based controller for positive linear systems subject to unknown inputs. We propose a new approach to numerically compute the controller, which is obtained via a function of the state to be estimated with the same order as the controller. The positive observer-based controller, is obtained into two steps. First, we search for a positive state feedback gain for the design of a control law such that the closed-loop is positive and asymptotically stable. This synthesis of state-feedback gain is solved using Linear Programming (LP) technique. Then, we search in a second step for a positive functional observer-based controller permitting to reconstruct this control law, and so to estimate only a functional of the state useful for control purposes. The observer is positive, that is, it ensures the nonnegativity of the estimated states. The proposed procedure is based on the positivity of an augmented system composed of dynamics of both considered system and proposed observer-based controller and also, on the unbiasedness of the estimation error by the resolution of Sylvester equation. Then existence conditions of such observer-based controller are formulated in terms of linear programming (LP) problem, where we use the Perron-Frobenius theorem applied to Metzler matrices. An algorithm that summarizes the different steps of the designed positive controller for positive linear systems is given. Finally, a numerical example and simulation results are given to illustrate the effectiveness of the proposed method.

This paper introduces an innovative and efficient method for estimating the state functional of linear discrete-time systems with delays that are subject to actuator and sensor faults, which are represented by unknown inputs. The aim of this paper is twice: first, to design a robust observer that accurately and stably estimates the considered state functional; and second, to reconstruct the inputs responsible for the occurrence of faults. We develop the observer dynamics using Lyapunov–Krasovskii theory. This theory enables us to construct an appropriate functional that ensures convergence, stability, and robustness of the estimation error despite the presence of delays. The proposed strategy provides a systematic framework for fault estimation and compensation in delayed discrete systems. To evaluate the method’s validity and efficiency, we apply it to a disinfection tunnel system benchmark. The simulation results clearly demonstrate the observer’s satisfactory performance and accurate estimation of the state functional and unknown fault inputs.

This paper deals with the design of a positive functional H∞ filter-based controller for positive linear systems subject to bounded energy disturbances. We propose a new approach to numerically compute the controller, which is obtained via a function of the state to be estimated with the same order as the controller. The positive filter-based controller is obtained into two steps. First, we search for positive state feedback gain for the design of a control law such that the closed-loop is positive, stable and ensures an H∞ performance requirement. This design of state-feedback gain is solved via constrained Linear Matrix Inequalities (LMIs). Then, we search in a second step for a positive functional filter-based controller permitting to reconstruct this control law, and so to estimate only a functional of the state useful for control purposes. The filter is positive, that is, it ensures the nonnegativity of the estimated states. The proposed procedure is based on the positivity of an augmented system composed of dynamics of both considered system and proposed filter-based controller and also, on the unbiasedness of the estimation error by solving a Sylvester equation. Then we derive conditions for the establishment of such filterbased controller in terms of an optimization problem, that can be solved via constrained LMIs. An algorithm that summarizes the different steps of the designed positive controller for positive linear systems is given. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

This paper proposes a new principal component analysis (PCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method which maximizes the model sensitivity to a certain process fault type is proposed. This method uses normal operating or known faulty data to build the PCA model and other faulty data to fix its structure. A new structuration method is proposed to identify the process fault. This method computes the common angles between the residual subspaces of the different modes. It generates a reduced set of detection indices that are sensitive to certain process faults and insensitive to others. The proposed FDI scheme is successfully applied to the Tenessee Eastman process (TEP) supposedly affected by several process faults.

This paper proposes a functional observer for a bilinear singular systems with constant time delay present in both state and known input vectors. The considered system has transformed to an updated form in order to estimate both a state vector and an unknown inputs functional vector independently from the considered time delay. The necessary conditions for the existence of the functional observer are formulated. The proposed observer design procedure is based on the Lyapunov–Krasovskii theory. Numerical example is given to prove the new approach.

In this paper, we will propose a solution to estimate the defect at the input of a non-linear system based on the ARX-Laguerre multimodel. So, we will present the design of the PI (Proportional Integral) observer based on the ARX-Laguerre multimodel. and the gains of which will be calculated by an algorithm for detecting and estimating input faults in non-linear systems. The detection and estimation algorithm is based on the Linear Matrix Inequality (LMI) optimization method.

The operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (ML) technique for fault detection and diagnosis (FDD) of uncertain HVAC systems. The main goal of the developed MSIPCA-ML approach is to enhance the diagnosis performance, improve the indoor environment quality, and minimize the energy consumption in uncertain building systems. The model uncertainty is addressed by considering the interval-valued data representation. The performance of the proposed FDD is investigated using sets of synthetic and emulated data extracted under different operating conditions. The presented results confirm the high-efficiency of the developed technique in monitoring uncertain HVAC systems due to the high diagnosis capabilities of the interval feature-based support vector machines and k-nearest neighbors and their ability to distinguish between the different operating modes of the HVAC system.

Kernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with measurements. Therefore, in this paper, two different interval reduced KPLS (IRKPLS) models are developed for monitoring large scale nonlinear uncertain systems. The proposed IRKPLS models present an interval versions of the classical KPLS model. The two proposed IRKPLS models are based on the Euclidean distance between interval-valued observations as a dissimilarity metric to keep only the more relevant and informative samples. The first proposed IRKPLS technique uses the centers and ranges of intervals to estimate the interval model, while the second one is based on the upper and lower bounds of intervals for model identification. These obtained models are used to evaluate the monitored interval residuals. The aforementioned interval residuals are fed to the generalized likelihood ratio test (GLRT) chart to detect the faults. In addition to considering the uncertainties in the input-output systems, the new IRKPLS-based GLRT techniques aim to decrease the execution time when ensuring the fault detection performance. The developed IRKPLS-based GLRT approaches are evaluated across various faults of the well-known Tennessee Eastman (TE) process. The performance of the proposed IRKPLS-based GLRT methods is evaluated in terms of missed detection rate, false alarms rate, and execution time. The obtained results demonstrate the efficiency of the proposed approaches, compared with the classical interval KPLS.

The control of nonlinear uncertain systems is still an open area of research, and sliding mode control (SMC) is one of the robust and effective methods to cope with uncertain conditions. In this paper, a new sliding mode control algorithm for a class of discrete-time nonlinear uncertain systems is proposed. By using an estimator of uncertainties and external disturbances, the proposed algorithm ensures the stability of the closed loop system as well as the reference tracking. The controller designed using the above technique is completely insensitive to the parametric uncertainty and the external disturbances. Simulations are carried out on a numerical example and a bioreactor benchmark, and the results confirm the effectiveness of our proposition.

This study proposes a new kernel method for online identification of a non‐linear system modelled on reproducing kernel Hilbert space (RKHS). The proposed method is a concatenation of two techniques proposed in the literature, the support vector regression and the Regularisation Networks (RNs). The proposed algorithm, called the online SVR‐RN kernel method, uses first the SVR in an offline phase to construct an RKHS model with a reduced parameter number and second the RN method in an online phase to update the model parameters. The proposed algorithm has been tested to identify the chemical Tennessee Eastman Process and the electronic non‐linear system with a Wiener Hammerstein structure.