Sequential MPC Strategy for High Performance Induction Motor Drives: a detailed analysis

Abstract

The paper deals with a newly developed sequential model predictive control strategy for the highperformance control of electric drives. The sequential nature of cost function evaluation allows to eliminate weighting factors whose tuning is not straightforward. In the first cost function evaluation, torque (or flux) error is minimized and, the second evaluation minimizes the flux (or torque) error. The first optimization generates two optimal voltage vectors that give minimum error for the controlled variable and the second optimization tests only the selected two vectors to find the global optimal. In this paper, a detailed analysis of the sequential MPC is carried out with a focus on the inversion of sequence of optimization with respect to the original algorithm. The paper also analyses the effect of selecting more than two vectors from the first evaluation and explains to the reader why some numbers of selected vectors produce flux and torque distortions while others do not control flux and torque at all

Sensorless Cascaded-Model Predictive Control applied to a Doubly Fed Induction Machine

“…The objective function can be defined in a way to optimize multiple variables prioritizing their importance by means of weighting factors usually tuned by trial and error procedure. The tuning of weighting factors is the most demanding aspect of scalar cost function-based Model Predictive Control which want to be avoid; in [8] the effort of the weighting factor selection has been avoided by decoupling the control variables considering two cascade cost functions to optimize the individually. Nevertheless, this strategy is still affected by having unwanted non-constant switching frequency and high ripples.…”

“…In order to tackle the non-constant switching frequency issue, in [12] a further stage has been introduced to calculate the duty cycle of the previous selected optimum vectors, in order to fulfil constant switching frequency, and visible improvement can be observed on the state variables ripple. This paper proposes an alternative to the method exploited in Riccio et al (2018) by considering two cascade objective functions as in Vodola et al (2019), but introducing a different duty cycle calculation criteria; this paper proposes a Modulated and Cascade Model Predictive Control (C-MPC) strategy which consists on two sequential objective functions, which operate to control the desired variables, without the need of defining weighting factors. In the first objective function, two optimum voltage vectors are selected among the eight feasible configurations of a two-level voltage source inverter (2L-VSI), to optimize the first cost function, defined to track the first control variable which will be one of the current components represented in the synchronous reference frame.…”

“…The state vector x(t) consists of the stator and rotor currents in the synchronous reference frame "dq", the rotor rotational speed ω r , the rotor angle θ r and the load torque T L . The input, output and state vectors components are listed in (8).…”