Data compression techniques applied to high resolution high frame rate video technology

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended

Sensorless Rotor Position Estimation For Brushless DC Motors

Brushless DC motor speed is controlled by synchronizing the stator coil current with rotor position in order to acquire an accurate alignment of stator rotating field with rotor permanent-magnet field for efficient transfer of energy. In order to accomplish this goal, a motor shaft is instantly tracked by using rotating rotor position sensors such as Hall effect sensors, optical encoders or resolvers etc. Adding sensors to detect rotor position affects the overall reliability and mechanical robustness of the system. Therefore, a whole new trend of replacing position sensors with sensorless rotor position estimation techniques have a promising demand. Among the sensorless approaches, Back-EMF measurement and high frequency signal injection is the most common. Back-EMF is an electromotive force, directly proportional to the speed of rotor revolutions per second, the greater the speed motor acquires the greater the Back-EMF amplitude appears against the motion of rotation. However, the detected Back-EMF is zero at start-up and does not provide motor speed information at this instant. There-fore, Back-EMF based techniques are highly unfavourable for low speed application specially near zero. On the other hand, signal injection techniques are comparatively developed for low or near zero motor speed applications and they also can estimate the on-line motor parameters exploiting the identification theory on phase voltages and currents signals. The signal injection approach requires expensive additional hardware to inject high frequency signal. Since, motors are typically driven with pulse width modulation techniques, high frequency signals are naturally already present which can be used to detect position. This thesis presents rotor position estimation by measuring the voltage and current signals and also proposes an equivalent permanent-magnet synchronous motor model by fitting thedata to a position dependent circuit model

Model predictive current control of switched reluctance motor with inductance auto-calibration

The thesis is composed of three papers, which investigate the application of Model Predictive Controller (MPC) for current control of Switched Reluctance Motor (SRM). Since the conventional hysteresis current control method is not suitable for high power SRM drive system with low inductance and limited switching frequency, MPC is a promising alternative approach for this application. The proposed MPC can cope with the measurement noise as well as uncertainties within the machine inductance profile. In the first paper, a MPC current control method for Double-Stator Switched Reluctance Motor (DSSRM) drives is presented. A direct adaptive estimator is incorporated to follow the inductance variations in a DSSRM. In the second paper, the Linear Quadratic (LQ) form and dynamic programming recursion for MPC are analyzed, afterwards the unconstrained MPC solution for stochastic SRM model is derived. The Kalman filter is employed to reduce the variance of measurement noises. Based on Recursive Linear-Square (RLS) estimation, the inductance profile is calibrated dynamically. In the third paper, a simplified recursive MPC current control algorithm for SRM is applied for embedded implementation. A novel auto-calibration method for inductance surface estimation is developed to improve current control performance of SRM drive in statistic terms. --Abstract, page iv

Advanced Modeling of Anisotropic Synchronous Machine Drives for Sensorless Control

Synchronous machines are extensively used for home appliances and industrial applications thanks to their fast dynamic response, good overload capability and high energy density. A precise knowledge of the rotor position is required to control efficiently this kind of motors. In most of the applications resolvers or absolute encoders are installed on the rotor shaft. The employment of position sensors leads to significant drawbacks such as the increased size and cost of the system and a lower reliability of the drive, caused by additional hardware and cabling. In sensorless drives motor position is estimated and employed in the machine control. Thus, no position sensor is required by the drive and all the drawbacks entailed by the sensor are eliminated. Moreover, the position estimation could be useful for redundancy in case of system failures. Therefore, position estimation techniques are object of great interest in the electric drives field. Position estimation techniques can be divided into two main categories: methods that are suitable for medium or high speed and techniques suitable for low speed or standstill operations. In the former group the motor position is estimated through a reconstruction of the permanent magnet flux or back electromotive force (back-EMF). In case of synchronous reluctance machines it is possible to reconstruct the extended active flux or back-EMF. Stator voltages and currents measurements are needed for these reconstruction methods. Since these signals amplitude is proportional to the rotor speed, position estimation can be successfully performed only for medium and high speed machine operations. In the low speed range, sensorless schemes exploit the rotor magnetic anisotropy. Thus, position can be estimated only for anisotropic motors, i.e. synchronous reluctance motors (SynRM), permanent magnet assisted synchronous reluctance motors (PMA-SynRM) and interior permanent magnet synchronous motors (IPMSM). The rotor anisotropy is recognized thanks to an high frequency voltage injection in the stator windings. Several injection techniques have been proposed, differing from the signal typology. In particular, high frequency sinusoidal or square-wave carriers are often applied. The position information is usually extracted from the current response through a heterodyning demodulation that entails the use of low pass filters in the position estimator, limiting its dynamic. The aim of the research was proposing a new algorithm to estimate the rotor position from the HF current response, getting rid of the demodulation and its weaknesses. Thus, the ellipse fitting technique has been proposed. Robustness against signal processing delay effects and a reduced number of required filters are the main advantages of this novel approach. The inverse problem related to the ellipse fitting is solved implementing a recursive least squares algorithm. The proposed ellipse fitting technique is not affected by signal processing delay effects, and it requires the tuning of only one parameter, called forgetting factor, making the studied method suitable for industrial application thanks to its minimal setup effort. Besides the ellipse fitting technique for rotor position estimation, two other topics have been studied: - Computation of self-sensing capabilities of synchronous machines. - Online incremental inductances identification for SynRM.Synchronous machines are extensively used for home appliances and industrial applications thanks to their fast dynamic response, good overload capability and high energy density. A precise knowledge of the rotor position is required to control efficiently this kind of motors. In most of the applications resolvers or absolute encoders are installed on the rotor shaft. The employment of position sensors leads to significant drawbacks such as the increased size and cost of the system and a lower reliability of the drive, caused by additional hardware and cabling. In sensorless drives motor position is estimated and employed in the machine control. Thus, no position sensor is required by the drive and all the drawbacks entailed by the sensor are eliminated. Moreover, the position estimation could be useful for redundancy in case of system failures. Therefore, position estimation techniques are object of great interest in the electric drives field. Position estimation techniques can be divided into two main categories: methods that are suitable for medium or high speed and techniques suitable for low speed or standstill operations. In the former group the motor position is estimated through a reconstruction of the permanent magnet flux or back electromotive force (back-EMF). In case of synchronous reluctance machines it is possible to reconstruct the extended active flux or back-EMF. Stator voltages and currents measurements are needed for these reconstruction methods. Since these signals amplitude is proportional to the rotor speed, position estimation can be successfully performed only for medium and high speed machine operations. In the low speed range, sensorless schemes exploit the rotor magnetic anisotropy. Thus, position can be estimated only for anisotropic motors, i.e. synchronous reluctance motors (SynRM), permanent magnet assisted synchronous reluctance motors (PMA-SynRM) and interior permanent magnet synchronous motors (IPMSM). The rotor anisotropy is recognized thanks to an high frequency voltage injection in the stator windings. Several injection techniques have been proposed, differing from the signal typology. In particular, high frequency sinusoidal or square-wave carriers are often applied. The position information is usually extracted from the current response through a heterodyning demodulation that entails the use of low pass filters in the position estimator, limiting its dynamic. The aim of the research was proposing a new algorithm to estimate the rotor position from the HF current response, getting rid of the demodulation and its weaknesses. Thus, the ellipse fitting technique has been proposed. Robustness against signal processing delay effects and a reduced number of required filters are the main advantages of this novel approach. The inverse problem related to the ellipse fitting is solved implementing a recursive least squares algorithm. The proposed ellipse fitting technique is not affected by signal processing delay effects, and it requires the tuning of only one parameter, called forgetting factor, making the studied method suitable for industrial application thanks to its minimal setup effort. Besides the ellipse fitting technique for rotor position estimation, two other topics have been studied: - Computation of self-sensing capabilities of synchronous machines. - Online incremental inductances identification for SynRM

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications

Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system

Study of rotor position estimation algorithm based on back-EMF voltage for dual-winding fault-tolerant permanent magnet motor

An improved position estimation of the sensorless control system with online parameter identification based on back-Electromotive Force (EMF) voltage is presented for the dual-winding fault-tolerant permanent magnet motor (FTPMM). In this control system, the rotor position is estimated by the flux linkage and the back-EMF which are generated by each phase winding. By introducing phase-locked loop technology to compensate the steady-state error of the system and online identification of motor parameters which is using the least-square method with forgetting factor, more accurate position estimation can be obtained. The current vector fault-tolerant control strategy improves the fault tolerance of the system and makes it strong robust stability. The simulation results have shown that the accurate position data can be acquired both under healthy condition and single-phase fault condition. Then, the hardware experimental results show the feasibility and validity of the proposed algorithm

Adaptive Control for the Position of Magnetic Particles using Magnetic Traps

Magnetic traps are an important instrument for analyzing the behavior of systems and biological processes. They manipulate magnetic particles by applying a force under the influence of magnetic fields. Controlling the position of the magnetic particle for single molecule studies is difficult due to the complexity of the instrument because its dynamics can change per experiment. This results in users spending an immense amount of time designing compensators to meet experimental requirements, yielding insufficient time spent concentrating on the experiment.One method to alleviate users of designing compensators is to incorporate adaptive control methods into the design of magnetic traps. Adaptive control is able to adjust the parameters of the compensator to ensure the performance of the instrument meets specific requirements. The magnetic particle constantly moves from the Brownian disturbances acting upon it. These disturbances can be minimized by using an adaptive Q-parametrized compensator structure with LMS to minimize a frequency weighted version of the displacement of the magnetic particle for low frequencies.An adaptive Q-parametrized compensator structure was incorporated into the design of the magnetic trap, resulting in the position of the magnetic particle being stabilized, the effects of the Brownian disturbances being reduced, and the dynamics of the instrument changing into account. The displacement of the magnetic particle due to the Brownian disturbances was suppressed more as the number of FIR weights increased than using the nominal adaptive compensator