Accurate Inverter Error Compensation and Related Self-Commissioning Scheme in Sensorless Induction Motor Drives

This paper presents a technique for accurately identifying and compensating the inverter nonlinear voltage errors that deteriorate the performance of sensorless field-oriented controlled drives at low speed. The inverter model is more accurate than the standard signum-based models that are common in the literature, and the self-identification method is based on the feedback signal of the closed-loop flux observer in dc current steady-state conditions. The inverter model can be identified directly by the digital controller at the drive startup with no extra measures other than the motor phase currents and dc-link voltage. After the commissioning session, the compensation does not require to be tuned furthermore and is robust against temperature detuning. The experimental results, presented here for a rotor-flux-oriented SFOC IM drive for home appliances, demonstrate the feasibility of the proposed solution

Data-driven online temperature compensation for robust field-oriented torque-controlled induction machines

Squirrel-cage induction machines (IMs) with indirect field-oriented control are widely used in industry and are frequently chosen for their accurate and dynamic torque control. During operation, however, temperature rises leading to changes in machine parameters. The rotor resistance, in particular, alters, affecting the accuracy of the torque control. The authors investigated the effect of a rotor resistance parameter mismatch in the control algorithm on the angular rotor flux misalignment and the subsequent deviation of stator currents and motor torque from their setpoints. Hence, an online, data-driven torque compensation to eliminate the temperature effect is proposed to enable robust torque-controlled IMs. A model-based analysis and experimental mapping of the temperature effect on motor torque is presented. A temperature-torque lookup-table is subsequently implemented within the control algorithm demonstrating the ability to reduce the detrimental effect of temperature on torque control. Experimental results on a 5.5 kW squirrel-cage induction motor show that the proposed data-driven online temperature compensation method is able to reduce torque mismatch when compared to having no temperature compensation. Up to 17% torque mismatch is reduced at nominal torque and even up to 23% at torque setpoints that are lower than 20% of the nominal torque. A limited torque error of <1% remains in a broad operating range

Ugađanje otpora rotora vektorski upravljanog indukcijskog motora korištenjem TS neizrazite logike

In this paper, we focus on the estimation of the rotor resistance to online tune the controllers in case of the Indirect Rotor Field Orientation Control (IRFOC) of Induction Machine (IM). The proposed method is based on the development of an adaptive Takagi-Sugeno (TS) fuzzy flux observer, described in a d-q synchronous rotating frame, to concurrently estimate the IM states and the rotor resistance variation. An investigation of the local pole placement is carried out in order to guarantee both the stability and specified observer dynamic performances. The observer\u27s gains design is based on the resolution of sufficient conditions driven into LMIs terms (Linear Matrix Inequalities). Simulation and experimentation are carried out to show the effectiveness of the proposed results.U ovom radu fokusiramo se na estimaciju otpora rotora za ugađanje parametera kontrolera tijekom rada indukcijskog motora (IM) upravljanog metodom indirektne kontrole orijentacije polja rotora (IRFOC). Predložena metoda je bazirana na razvoju adaptivnog Takagi-Sugeno (TS) neizrazitog obzervera toka, opisanog u d-q sinkronom rotacijskom okviru, kako bi se istovremeno estimirala stanja i varijacije otpora rotora IM-a. Provedeno je istraživanje lokalnog postavljanja polova kako bi se osigurala stabilnost i zadane dinamičke performanse obzervera. Dizajn pojačanja estimatora baziran je na rješenju dovoljnog broja uvjeta izraženih pomoću LMN izraza (linearne matrične nejednakosti). Simulacija i eksperimenti su provedeni kako bi se pokazala ispravnost predloženih rezultata

A Methodology for Solving the Equations Arising in Nonlinear Parameter Identification Problems: Application to Induction Machines

This dissertation presents a method that can be used to identify the parameters of a class of systems whose regressor models are nonlinear in the parameters. The technique is based on classical elimination theory, and it guarantees that the solution for the parameters which minimize a least-squares criterion can be found in a finite number of steps. The proposed methodology begins with an input-output linear overparameterized model whose parameters are rationally related. After making appropriate substitutions that account for the overparameterization, the problem is transformed into a nonlinear least-squares problem that is not overparameterized. The extrema equations are computed, and a nonlinear transformation is carried out to convert them to polynomial equations in the unknown parameters. It is then show how these polynomial equations can be solved using elimination theory using resultants. The optimization problem reduces to a numerical computation of the roots of a polynomial in a single variable. This nonlinear least-squares method is applied to the identification of the parameters of an induction motor. A major difficulty with the induction motor is that the rotor’s state variables are not available measurements so that the system identification model cannot be made linear in the parameters without overparameterizing the model. Previous work in the literature has avoided this issue by making simplifying assumptions such as a “slowly varying speed”. Here, no such simplifying assumptions are made. This method is implemented online to continuously update the parameter values. Experimental results are presented to verify this method. The application of this nonlinear least-squares method can be extended to many research areas such as the parameter identification for Hammerstein models. In principle, as long as the regressor model is such that the system parameters are rationally related, the proposed method is applicable

Min-Max Predictive Control of a Five-Phase Induction Machine

In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for the predictive stator current control of a five-phase induction machine (IM). The min-max operator is explored for the first time as an alternative to the traditional loss function. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the loss function and require a cumbersome procedure to tune. In order to cope with conflicting criteria, the proposal uses a decision function that compares predicted errors in the torque producing subspace and in the x-y subspace. Simulations and experimental results are provided, showing how the proposal compares with the traditional method of fixed tuning for predictive stator current control.Ministerio de Economía y Competitividad DPI 2016-76493-C3-1-R y 2014/425Unión Europea DPI 2016-76493-C3-1-R y 2014/425Universidad de Sevilla DPI 2016-76493-C3-1-R y 2014/42

A New Induction Motor Adaptive Robust Vector Control based on Backstepping

In this paper, a novel approach to nonlinear control of induction machine, recursive on-line estimation of rotor time constant and load torque are developed. The proposed strategy combines Integrated Backstepping and Indirect Field Oriented Controls. The proposed approach is used to design controllers for the rotor flux and speed, estimate the values of rotor time constant and load torque and track their changes on-line. An open loop estimator is used to estimate the rotor flux. Simulation results are presented which demonstrate the effectiveness of the control technique and on-line estimation