Contrôle

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Comparative study of three nonlinear observers
(2021) Terkmane, Mustapha; LouahchiI, Zakarya; Akroum, Mohamed (supervisor)
This report presents a comparison study of performances and characteristics of three advanced state observers, including the high-gain observers, the sliding-mode observers and the extended state observers. These observers were originally proposed to address the dependence of the classical observers, such as the Kalman Filter and the Luenberger Observer, on the accurate mathematical representation of the plant. The results show that, over all, the nonlinear extended state observer is much superior in dealing with dynamic uncertainties, disturbances and sensor noise. Several novel nonlinear gain functions are proposed to address the difficulty in dealing with unknown initial conditions. Simulation results are provided. Keywords: High Gain Observer, Sliding Mode Observer, Nonlinear Extended State Observer.
Comparison of three neural network controllers
(Université M'hamed Bougara Boumerdès: Institue de génie electronic et electric, 2024) Chadli, Rayane; Akroum, Mohamed (supervisor)
The integration of neural networks in control theories has the potential to revolutionize modern control engineering by enhancing performance measures and increasing plant efficiency. This project aims to explore the performance of three neural network controllers: Neural Network Predictive controller , Neural Network Model Reference Controller, and the Nonlinear Auto-Regressive Moving Average Controller and evaluate the potential benefits and challenge sassociated with each type of controller. A system simulation was created on Simulink and Matlab scripts to compare a set of performance measures. Data were collected from several simulations and averaged to provide estimates. The finding sindicat etha tneura lnetwork sca nsignificant lyimpro v eaplant ’sresponse and accuracy,especially the Nonlinear Auto-Regressive Moving Average (NARMA-L2) and Neural Network Model Predictive Controllers. However, challenges such as controller training and computational power suggest a need to optimize the network algo- rithms. While neural network controllers provided enhanced responses with minimal informa- tion about the plants,each type studied has a specifi cfie ld ofapplicati ona ndlimitations under certain conditions.
Final year project report presented in partial fulfilment of the requirements for the degree of
(2022) Bensalah, Takouallah Zoubida; Agraniou, Hania; Akroum, Mohamed (supervisor)
The goal of this report is to develop a model reference adaptive control system (MRAC) for aerospace vehicles that can enhance their fl ying qualities and cancel out or reject any type of pertur- bations in the control input channel as well as external environmental disturbances while the aircraft is in fl ight. Several strategies and approaches were investigated. The basic direct and indirect model reference adaptive control methods were introduced, followed by the robust modifi ed controllers 𝜎- modifi cation and 𝑒-modifi cation, as well as the 𝜇-modifi cation, which handles actuator restrictions. The model reference adaptive fl ight control system, referred to as the augmented model reference adaptive controller is based on the direct MRAC approach, which adjusts the system’s control law parameters by estimating its parameters directly was established. It utilizes two adaptive control ar- chitectures: Robust adaptive control based on 𝑒-mod to rend the system reliable when subjected to external disturbances, and constrained adaptive control based on 𝜇-mod, which assures stability in the presence of actuator amplitude saturation plus the elimination of the saturation. The stability study of all the mentioned controllers and approaches were covered using Lyapunov’s stability theorems as well as the uniform ultimate boundedness theorem.