Jedan novi postupak estimacije brzine vrtnje vektorski upravljanog asinkronog motora zasnovan na adaptivnom sustavu s referentnim modelom i neuronskim mrežama

This paper proposes a new sensorless technique for induction motor drives based on a hybrid MRAS-neural technique, which improves a previously developed neural MRAS based sensorless method. In this paper the open-loop integration in the reference model is performed by an adaptive neural integrator, enhanced here by means of a speed-varying filter transfer function. The adaptive model is based on a more accurate discrete current model based on the modified Euler integration, with a resulting more stable behaviour in the field weakening region. The adaptive model is further trained on-line by a generalized least squares technique, the MCA EXIN + neuron, in which a parameterized learning algorithm is used. As a consequence, the speed estimation presents an improved convergence with higher accuracy and shorter settling time, because of the better transient behaviour of the neuron. A test bench has been set up to verify the methodology experimentally and the results prove its goodness at very low speeds (below 4 rad/s) and in zero-speed operation.U članku se predlaže novi postupak estimacije brzine vrtnje elektromotornog pogona s vektorski upravljanim asinkronim motorom. Postupak se zasniva na hibridnom adaptivnom sustavu s referentnim modelom (MRAS) i neuronskim mrežama. Takav postupak poboljšava prethodno razvijeni estimacijski postupak također zasnovan na »neuronskom MRAS-u«. U radu je realizirana integracija u otvorenoj petlji u referentnom modelu pomoću adaptivnog neuronskog integratora unaprijeđenog s filtrom čija prijenosna funkcija ovisi o brzini motora. Adaptivni je model zasnovan na točnijem diskretnom strujnom modelu motora dobivenom modificiranom Eulerovom integracijom, što rezultira stabilnijim vladanju pogona u režimu slabljenja polja. Adaptivni je model nadalje on-line obučavan korištenjem poopćene metode najmanjih kvadrata (»MCA EXIN+neuron« postupak) pri čemu se koristi parametrirani algoritam učenja. Zbog boljeg ponašanja neurona u dinamičkim stanjima poboljšava se konvergencija estimacije brzine s većom točnošću i manjim vremenom smirivanja. Za eksperimentalnu provjeru predložene metode izgrađena je laboratorijska maketa. Dobiveni rezultati potvrđuju valjanost metode na veoma niskim brzinama (ispod 4 rad/s) i u režimu nulte brzine

Jedan novi postupak estimacije brzine vrtnje vektorski upravljanog asinkronog motora zasnovan na adaptivnom sustavu s referentnim modelom i neuronskim mrežama

This paper proposes a new sensorless technique for induction motor drives based on a hybrid MRAS-neural technique, which improves a previously developed neural MRAS based sensorless method. In this paper the open-loop integration in the reference model is performed by an adaptive neural integrator, enhanced here by means of a speed-varying filter transfer function. The adaptive model is based on a more accurate discrete current model based on the modified Euler integration, with a resulting more stable behaviour in the field weakening region. The adaptive model is further trained on-line by a generalized least squares technique, the MCA EXIN + neuron, in which a parameterized learning algorithm is used. As a consequence, the speed estimation presents an improved convergence with higher accuracy and shorter settling time, because of the better transient behaviour of the neuron. A test bench has been set up to verify the methodology experimentally and the results prove its goodness at very low speeds (below 4 rad/s) and in zero-speed operation.U članku se predlaže novi postupak estimacije brzine vrtnje elektromotornog pogona s vektorski upravljanim asinkronim motorom. Postupak se zasniva na hibridnom adaptivnom sustavu s referentnim modelom (MRAS) i neuronskim mrežama. Takav postupak poboljšava prethodno razvijeni estimacijski postupak također zasnovan na »neuronskom MRAS-u«. U radu je realizirana integracija u otvorenoj petlji u referentnom modelu pomoću adaptivnog neuronskog integratora unaprijeđenog s filtrom čija prijenosna funkcija ovisi o brzini motora. Adaptivni je model zasnovan na točnijem diskretnom strujnom modelu motora dobivenom modificiranom Eulerovom integracijom, što rezultira stabilnijim vladanju pogona u režimu slabljenja polja. Adaptivni je model nadalje on-line obučavan korištenjem poopćene metode najmanjih kvadrata (»MCA EXIN+neuron« postupak) pri čemu se koristi parametrirani algoritam učenja. Zbog boljeg ponašanja neurona u dinamičkim stanjima poboljšava se konvergencija estimacije brzine s većom točnošću i manjim vremenom smirivanja. Za eksperimentalnu provjeru predložene metode izgrađena je laboratorijska maketa. Dobiveni rezultati potvrđuju valjanost metode na veoma niskim brzinama (ispod 4 rad/s) i u režimu nulte brzine

Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

International audienceThis paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electricmotorsmakes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily

Sensorless Control of Induction Motors by the MSA based MUSIC Technique

This paper proposes a speed sensorless technique for induction motor drives based on the retrieval and tracking of the rotor slot harmonics (RSH). The RSH related to the rotor speed is first extracted from the stator phase current signature by the adoption of two cascaded ADALINEs (ADAptive Linear Element), whose output is the estimated slot harmonic. Then, the frequency of this slot harmonic as well as the speed is estimated by using minor space analysis (MSA) EXIN neural networks, which work on-line to iteratively compute the frequency of the slot harmonics based on MUSIC spectrum estimation theory. Thanks to its sample-based learning and the reduced mean square frequency estimation error, the speed estimation is fast and accurate. The proposed sensorless technique has been experimentally tested on a suitably developed test set-up with a 2-kW induction motor drive. It has been verified that this algorithm can track the rotor speed rapidly and accurately in a very wide speed range, working from rated speed down to 1.3 % of it

Some Permanent Magnet Synchronous Motor (PMSM) Sensorless Control Methods based on Operation Speed Area

This paper compares some sensorless Permanent Magnet Synchronous Motor (PMSM) controls for driving an electric vehicle in terms of operating speed. Sensorless control is a type of control method in which sensors, such as speed and position sensors, are not used to measure controlled variables.  The controlled variable value is estimated from the stator current measurement. Sensorless control performance is not as good as a sensor-based system. This paper aims are to recommend a control method for the PMSM sensorless controls that would be used to drive an electric vehicle. The methods that we will discuss are divided into four categories based on the operation speed area.  They are a startup, low speed, high speed, and low and high-speed areas. The low and high-speed area will be divided into with and without switching.  If PMSM more work at high speed, the most speed area that is used, we prefer to choose the method that works at high speed, that is, the modification or combination of two or more conventional methods

TOWARD A SOLUTION OF ALLOCATION IN LIFE CYCLE INVENTORIES: THE USE OF LEAST SQUARES TECHNIQUES

Purpose: The matrix method for the solution of the so-called inventory problem in LCA generally determines the inventory vector related to a specific system of processes by solving a system of linear equations. The paper proposes a new approach to deal with systems characterized by a rectangular (and thus non-invertible) coefficients matrix. The approach, based on the application of regression techniques, allows solving the system without using computational expedients such as the allocation procedure. Methods: The regression techniques used in the paper are (besides the ordinary least squares, OLS) total least squares (TLS) and data least squares (DLS). In this paper, the authors present the application of TLS and DLS to a case study related to the production of bricks, showing the differences between the results accomplished by the traditional matrix approach and those obtained with these techniques. The system boundaries were chosen such that the resulting technology matrix was not too big and thus easy to display, but at the same time complex enough to provide a valid demonstrative example for analyzing the results of the application of the above-described techniques. Results and discussion: The results obtained for the case study taken into consideration showed an obvious but not overwhelming difference between the inventory vectors obtained by using the least-squares techniques and those obtained with the solutions based upon allocation. The inventory vectors obtained with the DLS and TLS techniques are closer to those obtained with the physical rather than with the economic allocation. However, this finding most probably cannot be generalized to every inventory problem. Conclusions: Since the solution of the inventory problem in life cycle inventory (LCI) is not a standard forecasting problem because the real solution (the real inventory vector related to the investigated functional unit) is unknown, we are not able to compute a proper performance indicator for the implemented algorithms. However, considering that the obtained least squares solutions are unique and their differences from the traditional solutions are not overwhelming, this methodology is worthy of further investigation. Recommendations: In order to make TLS and DLS techniques a valuable alternative to the traditional allocation procedures, there is a need to optimize them for the very particular systems that commonly occur in LCI, i.e., systems with sparse coefficients matrices and a vector of constants whose entries are almost always all null but one. This optimization is crucial for their applicability in the LCI contex

Speed Sensorless Control with ANN and Fuzzy PI Adaptation Mechanism for Induction Motor Drive

In the speed sensorless induction motor drives system, the Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most common strategy. It suffers from parameter sensitivity and flux pure integration problems. As a result, it leads to the deterioration of speed estimation. Simultaneously, the traditional PI parameters design may cause speed estimation instability or have gross errors in the regenerative mode. To overcome above-mentioned problems, a suitable Artificial Neural Networks (ANN) based on Ant Colony Optimization (ACO) is presented to replace the reference model of the RF-MRAS. Furthermore, the ANN learning by the modified ACO can enhance the ANN convergence speed and avoids the trap of local minimum value of algorithm. In the meantime, a fuzzy PI adaptation mechanism is also put forward, so the proportional coefficient kp and the integral coefficient ki can be adjusted dynamically to adapt the speed variations. Finally, the simulation results suggest that the speed estimation is more accurate in both the dynamic and static process, and the stability of speed estimation in regenerative mode was improved

Comparative Study of Sensorless Control Methods of PMSM Drives

Recently, permanent magnet synchronous motors (PMSMs) are increasingly used in high performance variable speed drives of many industrial applications. This is because the PMSM has many features, like high efficiency, compactness, high torque to inertia ratio, rapid dynamic response, simple modeling and control, and maintenance-free operation. In most applications, the presence of such a position sensor presents several disadvantages, such as reduced reliability, susceptibility to noise, additional cost and weight and increased complexity of the drive system. For these reasons, the development of alternative indirect methods for speed and position control becomes an important research topic. Many advantages of sensorless control such as reduced hardware complexity, low cost, reduced size, cable elimination, increased noise immunity, increased reliability and decreased maintenance. The key problem in sensorless vector control of ac drives is the accurate dynamic estimation of the stator flux vector over a wide speed range using only terminal variables (currents and voltages). The difficulty comprises state estimation at very low speeds where the fundamental excitation is low and the observer performance tends to be poor. The reasons are the observer sensitivity to model parameter variations, unmodeled nonlinearities and disturbances, limited accuracy of acquisition signals, drifts, and dc offsets. Poor speed estimation at low speed is attributed to data acquisition errors, voltage distortion due the PWM inverter and stator resistance drop which degrading the performance of sensorless drive. Moreover, the noises of system and measurements are considered other main problems. This paper presents a comprehensive study of the different methods of speed and position estimations for sensorless PMSM drives. A deep insight of the advantages and disadvantages of each method is investigated. Furthermore, the difficulties faced sensorless PMSM drives at low speeds as well as the reasons are highly demonstrated. Keywords: permanent magnet, synchronous motor, sensorless control, speed estimation, position estimation, parameter adaptation

Optimal speed and torque estimations for improving the DTC dynamic performance of induction machines

High-performance AC drives require accurate speed, flux, and torque estimations to provide a proper system operation. Thus, this thesis proposes a robust observer, i.e. Extended Kalman Filter (EKF), to offer optimal estimations of these components in order to improve the dynamic performance of Direct Torque Control (DTC) of induction motor drives. The selection and quality of EKF covariance elements have a considerable bearing on the effectiveness of motor drives. Many EKF-based optimization techniques involve only a single objective for the optimal estimation of speed without giving concern to the other variables. In addition, the optimization is performed on a complicated EKF structure. Nevertheless, in this study, both speed and torque are concurrently estimated. The work presents a new method to investigate the selection of EKF filters by using a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) developed for resolving problems with multiobjectives. Filter element selection is the process of improving the concurrent estimation of speed and torque in order to increase EKF accuracy and allow higher drive efficiency. The proposed multi-optimal EKF-based estimation observer is used in combination with the sensorless direct torque control of induction motor. The investigated results for the multi-objective optimization indicate that the speed optimization gives superior performance when compared to the optimal torque. Owing to the large computation time of EKF algorithm, it increases the sampling time of DTC which leads to an increase in the motor torque ripples. The thesis proposes a Constant Frequency Torque Controller (CFTC) to replace the hysteresis torque controller that offers constant switching frequency and reduces torque ripples. Moreover, the CFTC has the capability of continuous switching regardless of speed variation; hence, leading to a consistent rotation of flux. Consequently, improvement on speed estimation, particularly at low and zero speed regions is accomplished and enhancement on the dynamic performance of torque is achieved when the reference speed change is applied from 0 rad/s, on the condition that the EKF observer is accurately optimized. To verify the improvements of the proposed methods, simulation and experimentation as well as comparison with the EKF-based DTC with the hysteresis controller are carried out