A A three-phase induction motor dynamic framework regulated by predictive and intelligent optimizations.

Resumo

The role of Model Predictive Control (MPC) as a fundamental optimization tool in modern control systems is increasingly emphasized. In this context, the paper presents Predictive Current Control (PCC) strategies for a three-phase inverter-fed induction motor drive (IM), focusing on two core approaches: the Finite Control Set (FCS) and the Integral Finite Control Set (IFCS). The FCS-MPC algorithm is based on the evaluation of a cost function, selecting a control signal from a finite set that satisfies the minimum value of the cost function. This cost function is calculated based on the squared error between the reference current and the measured stator current. Conversely, the I-FCS-MPC uses a cascade feedback structure with an appropriately adjusted controller gain to determine the optimal set of control variables. Using a minimization principle, these methods manage the switching states for reversal, causing the inverter to generate appropriate voltage signals for the induction motor. This article compares IM electromagnetic torque and load currents under each control technique to determine the most flexible and robust prediction strategy. All these methods were studied in the MATLAB/Simulink environment. In addition, the paper uses Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA) as benchmarks and shows that the results of FCS and I-FCS methods have superior performance.