Sensorless Control of Induction Machine Supplied by Current Source Inverter

This paper presents the use of the inherent high-frequency pulsewidth modulation (PWM) harmonics for sensorless control of ac machines. The amplitude and position of the PWM voltage harmonics cannot be controlled independently and are determined by the fundamental machine operation. However, they do form a high-frequency excitation and can provide information on saliencies within ac machines. This paper examines the feasibility of sensorless control based on extracting the rotor-bar slot position information for a cage induction machine using PWM harmonics. The position-signal demodulation and compensation schemes used are derived. Experimental results are provided for an off-the-shelf induction motor operating under sensorless current, speed, and position control, including zero excitation and zero speed.

In this paper, we describe the development of a sensorless control system that suppresses vibration of overhead crane loads. In general, loads carried by overhead cranes tend to vibrate and skillful and expert operation is required to avoid such vibration. This has led to considerable research into feedback control via a vibration sensor. However, the vibration sensor is initially very costly and requires a lot of maintenance work. This is why we began developing a sensorless control system. In this paper, we describe the outline and modeling of the overhead crane, then introduce a control design based on a simulation called IDCS (Inverse Dynamics Compensation via Simulation of feedback control systems). Subsequently, a DMM (Dual Model Matching) control method accreting features of feedforward and feedback controls, adopted when designing IDCS controller, is explained. The simulation result by discretizing controller and the controlling system design of actual overhead cranes also are described, whereupon the experimental result showing application to actual overhead cranes are shown.

Observability measurement and control strategy for induction machine sensorless drive in traction applications

This paper presents sensorless control at the critical point of operation existing at overlap of saliency frequencies which, so far has not been addressed by research. Considering induction machines, the resulting signals from injection based sensorless control methods are composed of saliency components caused by saturation, slotting or anisotropy, as well as inter-modulation effects. The points of operation at the spectral overlap of any two of the resulting signal's components are critical in terms of control performance as then a reliable separation is only possible feed forward. A well designed and trained neural network is applied to sequentially separate the different components. Up to now research focused on investigating zero speed, zero frequency operation neglecting the mentioned critical overlap area. In this paper first experimental results are presented verifying sustained sensorless operation at overlap of saliency signal's components.

This paper develops the brushless dc (BLDC) motor sensorless control system for an drilling dig system in shale gas development. The sensorless techniques that are based on a hysteresis comparator and inductance method with a high starting torque are suggested. Combined with the application of drilling rig system, analyzed the performance requirements of brushless DC motors (BLDCM) in the fields of low speed, and discussed the control method of the dr sensorless BLDCM, developed an prototype of sensorless BLDCM control system based on dsPIC33FJ32MC204. Started the motor in closed-loop mode by using inductance method and BEMF zero crossing detecting method, the motor speed range was effectively expanded. The experimental results showed that this control system could meet the requirements of low speed drilling dig and had application value.

The sensorless control system of permanent magnet synchronous motor (PMSM) fed by current source inverter is presented in this paper. The adaptive backstepping control system and the backstepping speed observer are presented. The control systems are based on multiscalar variables. The control variables are dc-link voltage and the output current vector pulsation. The control system was named the voltage control system because of the fact that the first control variable is voltage in dc-link. The machine inverter is treated as a commutator. It works with constant modulation index. The proposed control system is robust on mechanical and electromagnetical parameters of the motor. The adaptive estimator of the stator resistance is introduced. Such an approach to control of the PMSM motor gives better performance. It is robust to dynamic uncertainties and does not require knowledge of the system parameters. The mathematical equation was introduced. Simple method limitation of control variables is explained. The reference variables must be limited to a constant or various value if the motor system has to be used in the industrial applications. The simulation and experimental test are shown.

This study proposes a high-performance sensorless interior permanent-magnet synchronous motor control system using a high-frequency injection technique. A new estimation method that uses both the high-frequency d-axis current and q-axis current to estimate the rotor position is investigated. By using the proposed method, the hypothesis-synchronisation signal used in the conventional high-frequency injection method can be eliminated. A 1.25 kHz, 15 V, sinusoidal waveform voltage is injected into the d-axis synchronous frame of the motor and then the high-frequency d-axis and q-axis currents can be measured to determine the rotor position of the motor. In addition, a new compensation method is developed to reduce the estimation position error because of the mutual inductance and saturation effect of the motor. By using this new method, the estimated rotor-position error is within ±2 electrical degrees. A digital signal processor, TMS-320-F2812, is used as the estimating and control centre. The experimental results show that the proposed method can be applied in both a sensorless speed control system and a sensorless position control system. The sensorless speed control system has fast response, good load disturbance response and good tracking response.

An error correcting technique of an estimated position angle and rotating speed for the sensorless controlled salient-pole brushless DC motor drives is proposed. An unsymmetrical construction based on the mechanical or magnetic tolerance on the brushless DC motor appears as a modeling error of the motor. This error causes an estimating error which changes periodically. It is difficult to correct the error with hardware like filters. Then, a neuro-like fuzzy technique can he applied to correct the error of the estimated position angle. The position angle is estimated by a new AC current injection method. The errors of the estimated values change periodically, and unexpected cyclic speed errors generate when the motor is controlled as a speed servo system. The fuzzy model which estimates these errors is learned with a back propagation method which uses the cyclic errors as a teacher signal. Then, the system corrects the estimated position angles with the estimated error. The experimental results of the estimated position angle or speed are shown in the paper. From these results, the proposed sensorless control system could estimate more accurate position angle than conventional systems could.

In this paper, a novel sensorless control structure based on multi-scalar variables is proposed. The static feedback control law is obtained by using the multi-scalar variables transformation, where the multi-scalar variables approach allows a full linearization of the nonlinear system. The control system could be described as “optimized” because of the minimized number of controllers. Furthermore, control system is divided into two independent control systems which are synchronized by additional controllers. The first control system is responsible for the generation of the 1st fundamental harmonic, while the second control system is responsible for the generation of the 3rd harmonic. Both control systems are synchronized and ensure a trapezoidal shape of air gap induction waveform regardless of the machine operation point. In order to ensure a sensorless control system the rotor angular speed and the state variables are estimated by a speed observer. This speed observer is based on the backstepping approach. All analytical proposed assumptions are confirmed by simulations and experimental results.

Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44%, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.

Sensorless machine control at zero or low frequencies is typically achieved through external voltage step excitation which, by means of the current responses, produces quantifiable transient inductance values, containing the machine saliencies used to determine rotor position. However, additional excitation implies increasing current ripple, losses and acoustic noise, unacceptable for certain applications. The integration of these pulses into standard SVPWM has become, consequently, a key objective especially in the last decade. Nevertheless, the quality of the computed transient inductance, traditionally done by two sample points, is mainly constrained by the need of a minimum voltage excitation amount used for the current derivative. To deal with this limitations, this paper calculates, in online operation, the current derivative through a linear regression for different excitation strategies.

Sensorless control based on high frequency signal injection methods (HFSIM's) represent the interesting way to provide the high performances of the AC drives at low or near zero speed range. The HFSIM's exploit the machine saliency property which contains the information about the rotor position or flux position and track it by injecting a high frequency excitation. In this paper, a novel sensorless control strategy with the injection of a high-frequency rotating sinusoidal voltage into a stationary reference frame for an induction machine (IM) is proposed. First, the present paper exposes a model of the induction machine including slotting effects which it can be exploited in order to extract the harmonic saliency created by rotor slotting. Second, the high frequency signal injection method is used in order to allow tracking the characteristic harmonic saliency due to rotor slotting from which the rotor position and speed may be estimated. A High frequency voltage signal injection creates a high frequency current modulated by the saliencies presented inside the machine. To extract information about rotor position, the resulting stator current treads to heterodyning process to eliminate the disturbance components and subsequently the rotor position may be extracted by a closed-loop observer. Simulation results are shown in order to verify the validity of the proposal and to confirm the effectiveness of estimation method at zero frequency.

Sensorless Control of Induction Machines using a Reduced Order Extended Kalman Filter for Rotor Time Constant and Flux Estimation

This article presents a Permanent Magnet Synchronous Motor (PMSM) sensorless control system using backstepping control. The Extended State Observer (ESO) is also used to perform the sensorless control, and to estimate the load torque which is considered an external disturbance of the control system. Starting from the classic Field Oriented Control (FOC) type PMSM control structure, the classic PI speed and current controllers i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> , i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> are replaced by a backstepping control structure. The main blocks used for the implementation and numerical simulation in the Matlab/Simulink graphical programming environment are presented. The very good static and dynamic performance obtained through numerical simulations even at high speeds, step variations of the value of the load torque, uniformly distributed noise, and variations of the PSMS nominal parameters can be explained by the fact that the backstepping control is applied to the non-linear system of the PMSM and by the step-by-step method of achieving each iqref, ud and uq control by using a Lyapunov-type function. Future articles propose the approach of real-time implementation of the PMSM sensorless control by using the backstepping control.

Switched resistance motors, are becoming increasingly used in industrial and hybrid electric vehicle (HEV) applications. But improving SRM performance is still a crucial field for study, especially with regard to their inherent benefit of accurate rotor position estimate, which is closely related to operational effectiveness. Rotor position sensing has traditionally required the incorporation of specialized sensors, which has led to difficulties like increased costs, complicated alignment, limited size, and maintenance requirements. In order to overcome these constraints, this work promotes the creation of sensorless motor control schemes that make use of cutting edge Artificial intelligence methods such as fuzzy logic and Adaptive Neuro-Fuzzy Inference System (ANFIS).To provide robust control, the suggested sensorless control systems make use of inputs such bus voltage, rotor speeds, torque instructions, and SRM parameters. The effectiveness of these methods is thoroughly assessed by painstaking simulation experiments, confirming their capacity to attain accurate control and high-performance operation. To further illuminate the benefits of sensorless control implementations, this research also does a comparison analysis between the two suggested soft computing approaches and their sensor-based equivalents. In the end, this research advances the field of SRM technology, opening the door to more dependable, economical, and efficient motor control systems in a variety of industrial and automotive applications.