Browsing by Author "Bekhiti, Belkacem"

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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
Intelligent block spectral factors relocation in a quadrotor unmanned aerial vehicle
(Inderscience, 2017) Bekhiti, Belkacem; Dahimene, Abdelhakim; Hariche, Kamel; Nail, Bachir
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
Multivariate control system design using the theory of matrix polynomials
(2018) Bekhiti, Belkacem
The development of our mathematical work is based on state-space representations and matrix fraction descriptions as the mathematical models for physical systems. In this thesis, a design process is proposed to achieve block-structure assignment with a state-feedback and dynamic compensator for linear time invariant multiple inputs multiple outputs (MIMO) systems. A review of matrix polynomial theory is provided and a method to factorizing matrix polynomials into a complete set of linear factors has been developed. Furthermore, a study on feedback control has been undertaken; this includes a study on different feedback configuration and the development of the associated compensator equations. The input-output feedback configuration has been chosen to design the compensator which allows the placement of block roots of a desired denominator constructed from a desired latent structure. Moreover, a decoupling of the interactions between control loops in a multivariable plant has been developed; this proposed method is based on the ë-matrix (i.e. matrix polynomials) assignment. Finally, a novel MIMO intelligent predictive control design based on the matrix Diophantine equation resolution is presented, this later one is focalized on the adaptation mechanism made by merging the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Maximum Likelihood identification method to the MIMO predictive scheme for controlling an uncertain stochastic two-wheeled self-balancing robot without requiring prior knowledge of the exact parameters
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.
Parametric output feedback stabilization in MIMO systems : application to gas turbine power plant
(IEEE, 2017) Nail, Bachir; Kouzou, Abdellah; Hafaifa, Ahmed; Bekhiti, Belkacem
Robust block roots relocation via MIMO compensator design
(IEEE, 2017) Bekhiti, Belkacem; Dahimene, Abdelhakim; Nail, Bachir; Hariche, Kamel