MULTIOBJETIVE FINITE CONTROL SET MODEL PREDICTIVE TORQUE AND STATOR FLUX CONTROL OF AN INDUCTION MACHINE

Abstract

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During the last decades, basically two control strategies for electrical drives have dominated high-performance industrial applications: field-oriented control (FOC) and direct torque control (DTC). Nowadays, these control strategies are implemented on digital platforms. Digital signal processors (DSP) allows high flexibility, the integration of more functionality, and the implementation of more complex control schemes. Due to the development of powerful and fast microprocessors, increasing attention has been dedicated to the use of model predictive control (MPC) in power electronics. The first ideas about this strategy applied to power converters started in the 1980s. The main concept is based on the calculation of the future system behavior to compute optimal actuation variables. Due to the wide range ofMPC methods, the MPC techniques applied to power electronics have been classified into two main categories: classical MPC and finite control set MPC (FCS-MPC). In the first type, the control variable is the converter output voltage, in the form of a continuous duty cycle, while an open-loop receding horizon optimization problem is solved at every sampling step to calculate the best actuation. This actuation is applied usually using pulsewidth modulation (PWM) or space vector modulation (SVM). The second type, uses the inherent discrete nature of the power converter to solve the optimization problem using a single cost function. Here, the input is restricted to a finite set of discrete values. The discrete system model is evaluated for every possible actuation sequence and then compared with the reference in order to select the best voltage vector. The research done up to now has revealed that a key issue in FCS-MPC implementations is the selection of the weighting factors used in the cost function. Weighting factors are used to give more importance to one or another variable and to normalize the different control objectives. These scalar factors are parameters to adjust, and its selection is an important task because it is more complex than the tuning of proportional-integral (PI) coefficients or hysteresis bands of traditional controllers. Several methods using offline and online search procedures have been implemented at the present state of the art, but they are strongly dependent on the system parameters and they are formulated for two control objectives in a specific application only. When more objectives are considered, the weighting factors are usually obtained using trial and error procedures and running time-consuming simulations. The use of a single cost function to solve the optimization problem at each sampling time is not the only possible alternative. The possibility of the use of a different optimizer is the origin of this work. Different simple multiobjective optimization methods in order to eliminate the requirement of weighting factors in the predictive torque and flux control (PTC) scheme are presented. The optimization problem is solved using a multiobjective approach, giving rise to a multiobjective predictive torque and flux control. The scheme is then applied to an induction machine drive fed by a commercial two-level voltage source inverter (2L-VSI).