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Constrained Generalized Predictive Control Of An Induction Motor
Bektache Abdeldjebar,Benmahammed Khier 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
A constrained generalized predictive control (GPC) algorithm is presented for the non linear system. Generalized predictive control methods are gaining widespread acceptance industry-as they offer good performance based on simple step response or transfer -function which can be obtained experimentally a GPC.In this paper we are illustrated the GPC without constraint and output constrained controller to induction motor drive. The variable to be controlled are the rotor speed and flux trajectory .The load torque is considered as unknown disturbance. The simulation results show a good performance for the non linear system.
Control for Underactuated Systems Using Sliding Mode Observer
Djamila Zehar,Khier Benmahammed,Khalissa Behih 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.2
In this work, first we estimate all the system’s state vector, with guarantied precision, for a category of second order underactuated mechanical systems (UMS), exploiting the triangular observer (TO) model that suits to the structure of these systems. Then we propose a sliding mode controller (SMC). The latter uses the estimated states given by the observer. The underactuated system is decomposed into two subsystems, where the sliding surface is constructed in two levels for each subsystem. The proposed controller guaranties the tracking performances, with minimization of chattering phenomenon, due to the constructed observer, even for system with uncertainties. Simulation results show the effectiveness of this strategy of control.
Ahmed Chaouki Megherbi,Hassina Megherbi,Khier Benmahammed,Abdel Ghani Aissaoui,Ahmed Tahour 대한전기학회 2010 Journal of Electrical Engineering & Technology Vol.5 No.4
This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.