Finite-state Predictive Torque Control Research Articles

Enhanced FS-PTC: Dynamic Weighting Factors for Optimal Flux and Torque Control

Recently, Finite State-Predictive Torque Control (FS-PTC) of induction motors has gained significant interest in high-performance motor drive applications. The effectiveness of FS-PTC relies on the successful minimization of a cost function achieved by selecting an appropriate voltage vector. Typically, the cost function for FS-PTC is composed of errors between the predicted and reference values of torque and flux; hence a weighting factor, \mathbit{\lambda}, is normally employed to establish different priorities between torque and flux. However, determining the optimal is a complex undertaking, since an incorrect or suboptimal choice can needlessly compromise torque or flux responses. This paper introduces an online tuning approach for the weighting factor, based on the dynamic change of flux error. Instead of using a fixed value, the weighting factor is dynamically adjusted using a simple P or PI controller. The proposed method's performance is evaluated in this paper, considering various configurations of the controller's settings. Simulation results demonstrate that the proposed technique enhances the overall torque and flux ripples across a broad range of operating speeds, surpassing the performance of the fixed value weighting factor technique.

Enhanced Finite-State Predictive Torque Control of Induction Motor Using Space Vector Modulation

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Two‐Stage optimization based finite state predictive torque control for 3L‐NPC inverter Fed IM drives

The computational burden in Finite State Predictive torque control (FS-PTC) increases with the number of voltage vectors and number of variables in the cost function. The conventional FS-PTC for three level neutral-point clamped voltage source inverter (3L-NPC VSI) fed motor drive has a disadvantage of long execution time since all available vectors are evaluated in the prediction stage. This paper proposes a new method for reducing the number of voltage vectors from 27 to 17 in the prediction stage, which reduces the computational burden of conventional FS-PTC. The performance of the proposed voltage selection method is investigated for an induction motor drive in terms of electromagnetic torque and stator flux responses (transient and ripple), total harmonic distortion of stator currents, neutral point voltage variations, average switching frequency reduction and robustness of the drive. Experimental results confirm that the execution time is reduced by 30% compared to conventional FS-PTC while similar dynamic and steady-state performances are preserved.

Sensorless finite-state predictive torque control of induction motor fed by four-switch inverter using extended Kalman filter

PurposeDirect Torque Control (DTC) of induction motor drives is a well-established technique owing to features such as fast dynamic and insensibility to motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. To improve DTC performance, this study aims to propose and implement a sensorless finite-state predictive torque control using extended Kalman Filter in dSPACE environment.Design/methodology/approachThis paper deals with the design of an extended Kalman filter for estimating the state of an induction motor model and for sensorless control of systems using this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation.FindingsSimulation and experimental results reveal that the drive system, associated with this technique, can effectively reduce flux and torque ripples with better dynamic and steady state performance. Further, the proposed approach maintains a constant switching frequency.Originality/valueThe proposed speed observer have been developed and implemented experimentally under different operating conditions such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.

An improved finite‐state predictive torque control for switched reluctance motor drive

This study proposes an improved finite-state predictive torque control (FS-PTC) to minimise the torque ripples of switched reluctance motor (SRM) drive. Firstly, based on the accurate analytical method, a discrete time model is established for predicting the future states of SRM drive system. Secondly, to reduce the computational burden, a new switching table is constructed for the predictive controller by using the sector partition technique. Further, the torque ripple, copper losses, and average switching frequency are considered synchronously by using a multi-objective cost function. As a result, the proposed FS-PTC method not only can minimise the torque ripple but also can reduce effectively the copper losses and average switching frequency. Finally, the experimental results are carried out for a three phase 12/8 poles 1.5 kW SRM with the proposed control algorithm and the results are compared with conventional direct instantaneous torque control algorithm. These results demonstrate the effectiveness of the proposed method.

Selected Prediction Vectors Based FS-PTC for 3L-NPC Inverter Fed Motor Drives

Computational burden is a major hurdle for practical implementation of finite-state predictive torque control (FS-PTC) of motor drive fed by a multilevel inverter. One of the reasons of computational complexity is that all voltage vectors are evaluated for prediction and actuation. This paper proposes a reduced number of voltage vectors for the prediction and actuation, which are called prediction vectors in FS-PTC. The performance is investigated for a three-level neutral-point clamped inverter fed motor drive in terms of torque and flux response, stator current total harmonic distortion, robustness, average switching frequency, and neutral-point voltage variation. The number of prediction vectors is reduced based on the position of stator flux and the deviation in stator flux from its reference. Experimental results confirm that the computational burden could be reduced by 38%, while the dynamic performance is comparable with the conventional all voltage vectors based FS-PTC.

Low complexity predictive torque control strategies for a three‐level inverter driven induction motor

Iterative and complex prediction loop is a challenge for the implementation of finite-state predictive torque control (FS-PTC) of motor drive. The complexity is due to the complex torque calculations, number of available voltage vectors (which are called as prediction vectors), and weighting factor tuning for torque and flux errors in the cost function. One way to reduce the complexity is an equivalent reference stator flux vector calculation (RSFVC) from torque and flux references, which also solves the problem of weighting factor tuning. Along with a new stator flux based RSFVC technique, a reduced number of prediction vectors are proposed in this study to reduce the number of iteration of the prediction loop. The position of the stator flux and sign of the stator flux-error are considered to lessen the number of prediction vectors. Hence, the implementation challenges of FS-PTC algorithm are overcome. The performance of the proposed technique is investigated for two types of RSFVCs: one is based on the stator flux, and another one is based on the rotor flux. Experimental results verify that the proposed low complexity FS-PTC strategies retain the advantages of a conventional FS-PTC.

Predictive torque and flux control of an induction machine drive using fuzzy multi-criteria decision making

Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to optimize multiple objectives simultaneously. However, the implementation of FS-PTC for multiple objectives optimization requires the optimization of a single objective function, which is constructed using weighting factors as a linear combination of individual objective functions. Traditionally, the weighting factors are determined through a non-trivial process, which is a complex and time-consuming task. In an effort to avoid the time-consuming task of weighting factor selection, this paper aims at replacing the weighting factor calculation with a systematic fuzzy multiple-criteria decision making in which the individual objective functions may have equal or varying degrees of importance. As a result the weighting factor calculation can be completely avoided. The simulation and experimental tests are conducted on a 2.2 kW induction motor drive to validate the proposed approach. The result outcomes are compared with the conventional predictive torque control (PTC) using weighting factors on the same experimental platform.

Predictive Torque Control of Induction Motor Sensorless Drive Fed by a 3L-NPC Inverter

A finite-state predictive torque control system for a speed-sensorless induction motor drive supplied from a three-level neutral-point clamped inverter is proposed. For sensorless operation, the controller requires estimated speed and rotor/stator flux. In this study, the rotor speed and the rotor flux are estimated accurately by using an extended Kalman filter. Due to the large number of available voltage vectors, the control algorithm for a multilevel inverter-fed drive is computationally expensive. As a consequence, the controller requires longer execution time that yields worse torque, flux, and speed responses, especially at low-speed. In order to reduce the computational burden, a reduced number of voltage vectors for prediction and optimization in the control algorithm is proposed in this paper. The sign of the stator flux deviation and the position of the stator flux are predicted to lessen the number of voltage vectors tested. Experimental results illustrate that the proposed encoderless strategy can estimate the speed accurately over a wide speed range including field-weakening region while maintaining robustness and excellent torque and flux responses.

Finite-State Predictive Torque Control of Induction Motor Supplied From a Three-Level NPC Voltage Source Inverter

Finite-state predictive torque control (FS-PTC) of an induction motor (IM) drive has been widely investigated for two-level voltage source inverter recently. This control method suffers from high and variable switching frequencies in a wide range, due to the limited number of available voltage vectors of the power converter. In order to take advantage of multilevel inverter drives which offer the benefits of low harmonic distortion of the stator currents, torque ripple, and low switching frequency, this paper proposes the integration of the FS-PTC with a three-level neutral-point clamped (3L-NPC) inverter driven IM drive. The drawback inherited from the topology of 3L-NPC voltage source inverter (VSI), such as neutral-point voltage, is easily handled by treating it as a variable to the cost function. Similarly, apart from the inverter topology itself, the average switching frequency is reduced further, and is maintained almost constant over a wide speed range. The effectiveness of the proposed FS-PTC in terms of torque and flux responses, capacitor voltage balancing, and low average switching frequency is validated through experimental results.

A Simplified Finite-State Predictive Direct Torque Control for Induction Motor Drive

Finite-state predictive torque control (FS-PTC) is computationally expensive, since it uses all voltage vectors (VVs) available from a power converter for prediction and actuation. The computational burden is rapidly increased with the number of VVs and objectives to be controlled. Moreover, designing a cost function with more than two control objectives is a complex task. This paper proposes a simplified algorithm based on a new direct torque control (DTC) switching table to reduce the number of VVs to be predicted and objectives to be controlled. The new switching table also assists to reduce average switching frequency and its variation range. As a result, the cost function is simplified by not requiring to include the frequency term. Experimental results show that the average execution time and the average switching frequency for the proposed algorithm are greatly reduced without affecting the torque and flux performances achieved in the conventional FS-PTC.

A Speed-Sensorless FS-PTC of Induction Motors Using Extended Kalman Filters

A sensorless finite-state predictive torque control (FS-PTC) strategy uses stator current, estimated stator and rotor flux, and estimated rotor speed to predict stator flux and torque. Direct application of measured stator currents and using a noisy estimated speed in the prediction model degrade the steady-state performance in terms of higher current total harmonic distortion (THD), torque ripple, and flux ripple, particularly at low speeds. This paper proposes an extended Kalman filter (EKF)-based, which is a promising state observer, improved prediction model of sensorless FS-PTC for induction motor drives. The EKF has been used to estimate rotor speed, rotor/stator flux, and stator currents accurately. The estimated stator currents, instead of measured currents, are fed back to the prediction model, and thus, small stator current THD is confirmed. Depending on the commanded speed, either the rotor current model or the open-loop stator voltage model is proposed for the EKF to achieve better performance in a wide speed range, including the field-weakening region. The proposed control system has been verified experimentally, and excellent torque and flux responses, robustness, and stable operation at lower and higher speeds have been achieved.

An experimental assessment of finite-state Predictive Torque Control for electrical drives by considering different online-optimization methods

Finite-State Predictive Torque Control (FS-PTC) is experimentally investigated based on different online-optimization methods by using a two-level voltage source inverter for an induction machine. The calculation time and the switching frequencies are important research points for FS-PTC industrial applications. Long-step FS-PTC methods are expected to improve the performance of the system. However, the calculation time will increase exponentially with the increase of the prediction horizon. A reduced switching frequency PTC (RSF-PTC) method by considering the reductions of the switching frequency and the calculation time is tested. Based on this algorithm, an extended prediction horizon is proposed and verified on a common test bench. A torque-band based PTC (TB-PTC) method is proposed and discussed in this paper. The TB-PTC method pre-calculates the torque error between the predicted torque and the torque reference. The optimization method focuses on the flux error and the switching frequency for switching states which constrain the torque error within the torque-band. The conventional FS-PTC method, the RSF-PTC method with one-step and two-step horizons and the TB-PTC method are developed and experimentally compared in this work. The results confirm that conventional FS-PTC, RSF-PTC and TB-PTC methods can work well in the full speed range. When the switching frequencies and the calculation effort are taken into consideration, the RSF-PTC algorithm shows the better performance. However, the conventional FS-PTC method and the TB-PTC method have better current performance.

Encoderless Finite-State Predictive Torque Control for Induction Machine With a Compensated MRAS

An encoderless predictive torque control (PTC) is proposed in this paper. By using a rotor flux model reference adaptive system (MRAS) estimation method to PTC, the system has the virtue of low cost due to the absence of PWM and speed measurement components. PTC requires not only estimated speed but also estimated stator and rotor flux. In implementation, a compensated MRAS is considered for obtaining good flux estimations. The experimental results confirm that this control strategy has very fast dynamics, can adapt a very wide speed range, and shows good performance both at transient and steady states.