Browsing by Author "Hariche, Kamel"

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    A New 3D Sliding Pursuit Guidance Law for Fixed Wing Combat Drone Piloting: Application to El-Djazaïr 54
    (World Scientific, 2025) Bekhiti, Belkacem; Fragulis, George F.; Hariche, Kamel
    This paper introduces a new finite-time neural adaptive nonlinear 3D sliding pursuit guidance law designed for autonomous control of fixed-wing Unmanned Aerial Vehicles (UAVs) targeting a maneuvering object. The central innovation in the control strategy is the incorporation of sliding control in pure pursuit, which significantly enhances robustness against uncertainties and variations. Simulations were conducted using a specific combat drone model (El-Djazaïr 54), within a real-time virtual Simulation Platform for Aircraft Control System (SP-ACS). The control approach is model-based, with an initial identification phase before testing and validation. To identify unknown, variable, and classified aerodynamic parameters, the Total Least Squares Estimation (TLSE) method was employed. The mean values of aerodynamic coefficients were calculated, with any deviations treated as modeling uncertainties to be managed by the robust control law. Simulation results demonstrate that the El-Djazaïr 54 drone exhibits excellent performance in tracking the moving target and maintaining robustness despite modeling uncertainties
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    Extending the SRIV algorithm to LMFD models
    (2009) Akroum, Mohamed; Hariche, Kamel
    In this paper the Simplified Refined Instrumental Variable (SRIV) identification algorithm for SISO systems is extended to MIMO systems described by a Left Matrix Fraction Description (LMFD). The performance of the extended algorithm is compared to the well-known MIMO four-step instrumental variable (IV4) algorithm. Monte Carlo simulations for different signal to noise ratios are conducted to assess the performance of the algorithm. Moreover, the algorithm is applied to a simulated quadruple tank process
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    Helicopter flight control compensator design
    (2017) Yaici, Malika; Hariche, Kamel; Clarke, Tim
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    Intelligent block spectral factors relocation in a quadrotor unmanned aerial vehicle
    (Inderscience, 2017) Bekhiti, Belkacem; Dahimene, Abdelhakim; Hariche, Kamel; Nail, Bachir
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    MIMO identification and digital compensator design for quadruple tank process
    (IEEE, 2017) Bekhiti, Belkacem; Dahimene, Abdelhakim; Hariche, Kamel
    In this paper we have described a new design algorithm for the whole set of latent-structure assignment via the approaches of matrix polynomial placement with output noise rejection for a Quadruple tank process, of course the mathematical dynamic model of the process is obtained by MIMO (Simplified Refined Instrumental Variable) SRIV and/or MIMO (Linear Multi Stage Auto Regressive Moving Average with eXogenous input) LMS-ARMAX identification algorithms and then is handled and used in the control procedure which will provide us instead of placing only a set of desired eigenvalues we are able to assign both latent-vectors and the corresponding latent-values or more generally it is more efficient to assign the latent structure via the approach of Block pole placement
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    On eigenstructure assignment using block poles placement
    (Elsevier, 2014) Yaici, Malika; Hariche, Kamel
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    On The Block Decomposition and Spectral Factors of λ -Matrices
    (Arxiv, 2018) Bekhiti, Belkacem; Dahimene, Abdelhakim; Hariche, Kamel; Fragulis, George F.
    In this paper we factorize matrix polynomials into a complete set of spectral factors using a new design algorithm and we provide a complete set of block roots (solvents). The procedure is an extension of the (scalar) Horner method for the computation of the block roots of matrix polynomials. The Block-Horner method brings an iterative nature, faster convergence, nested programmable scheme, needless of any prior knowledge of the matrix polynomial. In order to avoid the initial guess method we proposed a combination of two computational procedures. First we start giving the right Block-QD (Quotient Difference) algorithm for spectral decomposition and matrix polynomial factorization. Then the construction of new block Horner algorithm for extracting the complete set of spectral factors is given.
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    Review of MIMO minimal realization techniques and case study on SCARA robot manipulator
    (Praise Worthy Prize, 2017) Cherifi, Karim; Hariche, Kamel
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    Robust block roots relocation via MIMO compensator design
    (IEEE, 2017) Bekhiti, Belkacem; Dahimene, Abdelhakim; Nail, Bachir; Hariche, Kamel
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    State feedback linearization using block companion similarity transformation
    (Institute of Advanced Engineering and Science, 2021) Kessal, Farida; Hariche, Kamel; Bentarzi, Hamid; Boushaki, Razika
    In this research work, a new method is proposed for linearizing a class of nonlinear multivariable system; where the number of inputs divides exactly the number of states. The idea of proposed method consists in representing the original nonlinear system into a state-dependent coefficient form and applying block similarity transformations that allow getting the linearized system in block companion form. Because the linearized system’s eigenstructure can determine system performance and robustness far more directly and explicitly than other indicators, the given class multivariable system is chosen. Examples are used to illustrate the application and show the effectiveness of the given approach