Contribution aux stratégies avancées de contrôle prédictif et adaptatif des machines asynchrones.

Abstract

The aim of this thesis was aimed at developing several advanced control strategies that will be applied to the rotor flux oriented vector control (IFOC) of the asynchronous machine (MAS). This work first presents an advanced control that combines adaptive control with fractional order PI (FOIP) regulators. The strategy uses the MIT and LYAPUNOV rule theory to adjust the control law of the reference model adaptive controller (MRAC), and then integrate it into the control law equations of the FOPI regulator. Secondly, we will present a new model of the RST polynomial corrector using a fractional calculus approach. The proposed controller will be applied to the IFOC for the speed control of the MAS which will be powered by a photovoltaic (PV) generator equipped with a maximum power point tracking (MPPT) algorithm. This model uses two fractional order controllers: the fractional order proportional-integral (FOPI) controller and the fractional order integralproportional (FOIP) controller, these controllers are combined with the generalized predictive control (GPC) technique. The GPC technique converts the continuous-time FOPI (and FOIP) controller into a discrete-time version, this conversion aims to ensure a fast response and efficient rejection of disturbances. Simulation tests are carried out to analyze the rotor speed and stator current ripples in order to evaluate the performance of the proposed method. The effectiveness of the proposed technique will be demonstrated through Matlab/Simulink. Finally, a new control strategy based on the combination of optimal model predictive control (OMPC) with fractional iterative learning control (F-ILC) for the speed regulation of MAS with application to electric vehicles is presented. MPC Optimal control uses predictive models to optimize speed control actions by considering the dynamic behavior of the MAS, when integrated with fractional order ILC, the system learns and refines speed control iteratively based on previous iterations, adapting to the specific characteristics of the MAS and improving performance over time.