Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques

Abstract

Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To improve the steady-state performance of the IM drives, this paper proposes an improved FCS-MPTC strategy, based on a novel fuzzy adaptive speed controller and an adaptive weighting factor, tuning strategy to reduce the speed, torque and flux ripples caused by different factors. Firstly, a discrete predicting plant model (PPM) with a new flux observer is established, laying the ground for achieving an FCS-MPTC algorithm accurately. Secondly, after analyzing the essential factors in establishing a fuzzy adaptive PI controller, with high ripple suppression capacity, an improved three-dimensional controller is designed. Simultaneously, the implementation procedures of the fuzzy adaptive PI controller-based FCS-MPTC are presented. Considering that a weighting factor must be employed in the cost function of an FCS-MPTC method, system ripples increase if the value of the weighting factor is inappropriate. Then, on that basis, a novel fuzzy adaptive theory-based weighting factor tuning strategy is proposed, with the real-time torque and flux performance balanced. Finally, both simulation and hardware-in-loop (HIL) test are conducted on a 1.1 kW IM drive to verify the proposed ripple reduction algorithms.

Adaptive Cost Function FCSMPC for 6-Phase IMs

“…In [3] the MPC formulation is used to include soft constraints for a nine-phase IM drive and the effect of cost function parameters is discussed. In [10] a fuzzy adaptive speed controller and adaptive weighting factors are used to reduce the speed, torque and flux ripples. Other proposals can be found in the review of [11].…”

“…Figure 8 shows the titeica surface fitted to data represented by Equation (10). Please notice that the upper part of the surface has been trimmed for ease of presentation.…”

“…The cases commented above are easy to implement as they just require selecting values of the design parameters λ xy and λ nc . These cases provide insight into the cubic surface and the underlying relationship of Equation (10). In particular, they show that the relationship between Λ and Γ is far from trivial.…”

Intelligent Permanent Magnet Motor-Based Servo Drive System Used for Automated Tuning of Piano

“…As for the proposed fuzzy adaptive controller, there are three main features w need to be addressed. Firstly, the fuzzy controller is a single-input-two-output cont This is not a typical two-dimensional controller that uses both the error of control va (position) and the differential of the error as the inputs [39]. This structure is reaso because in the piano tuning applications, the change rate of ∆θ is small enough to b tected when the position loop works.…”

“…Therefore, they must be converted to explicit values by using a defuzzification component. In engineering, the most commonly-used strategy is the centroid defuzzification which contains most of the inference results [39]:…”