Research on an inertial positioning system for a parallel kinematic machine

This paper addresses the development of an error reduction strategy for aninertial-sensor-based system for measuring the displacement of parallel kinematicmachine (PKM) struts and hence the tool centre point pose. The error reductionstrategy can significantly improve the performance of low-cost accelerometers andhas many potential generic inertial measurement applications. Inertialmeasurement systems are prone to errors introduced by manufacturing error,system set-up error and environmental factors. To accurately measuredisplacement the errors and noise contaminating the inertial data must beremoved. Based on data analysis, methods are developed in this paper to removebias error, and correct integration error through band limitation, errormodelling and velocity reconstruction. The velocity reconstruction algorithmdescribed significantly improves the performance of the system and is novelin its use of the bounded nature of the strut displacement. The inertialmeasurement system described is evaluated using a single PKM-Strut test bed forlinear movement and the experimental results are presented and analysed.

In this paper, a novel tool center point (TCP) interpolation method is proposed to generate smooth feedrate and tool trajectory for five-axis machining. TCP interpolator consists of two processes: block and look-ahead process. The block process determines the maximum feedrate of each block based on five-axis velocity equations, the movements of TCP and rotary axes. It detects sharp corners among NC blocks, and inserts arc segments into blocks with sharp corners to avoid tangential discontinuities. Corner feedrate is determined based on contour tolerance and kinematic constraints where velocity, acceleration and jerk on TCP and five-axis are all taken into consideration. The velocity profile of the TCP on each block is planned in the look-ahead process by means of TCP movement, corner velocity, velocity commands and jerk limits. Finally, simulations and experiments are carried out to validate the efficiency of the proposed method in improvement of the contour accuracy for five-axis machining.

Generalized reliability measures of Kalman filtering for precise point positioning

Laser trackers are widely used to measure kinematic tasks such as tracking robot movements.Common methods to evaluate the uncertainty in the kinematic measurement include approximations specified by the manufacturers, various analytical adjustment methods and the Kalman filter. In this paper a new, real-time technique is proposed, which estimates the 4D-path (3D-position + time) uncertainty of an arbitrary path in space. Here a hybrid system estimator is applied in conjunction with the kinematic measurement model. This method can be applied to processes, which include various types of kinematic behaviour, constant velocity, variable acceleration or variable turn rates. The new approach is compared with the Kalman filter and a manufacturer's approximations. The comparison was made using data obtained by tracking an industrial robot's tool centre point with a Leica laser tracker AT901 and a Leica laser tracker LTD500. It shows that the new approach is more appropriate to analysing kinematic processes than the Kalman filter, as it reduces overshoots and decreases the estimated variance. In comparison with the manufacturer's approximations, the new approach takes account of kinematic behaviour with an improved description of the real measurement process and a reduction in estimated variance. This approach is therefore well suited to the analysis of kinematic processes with unknown changes in kinematic behaviour as well as the fusion among laser trackers.

By the end of the next decade (1988), most large-area geodetic surveys will probably be made using a satellite Doppler positioning system, an inertial positioning system, or some combination of the two systems. The Doppler system is capable of determining the absolute, three-dimensional position of any point on the earth and it can also be used to obtain relative positions. Using data from multiple satellite passes and refined data reduction techniques, it is possible to obtain accuracies of 20 cm or better. The development of the Global Positioning System (GPS) in the 1980's is expected to improve position accuracies to 10 cm or better. The inertial system is capable of very large, rapid surveys with position accuracies of 20 cm or better relative to the control used. In addition, deflections of the vertical to one second of arc and gravity anomalies to one milligal can also be obtained. Despite relatively large costs, both systems have proven to be cost-effective because of their very high productivities.

BackgroundOpen-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification.MethodsLongitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation.ResultsThe logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression.ConclusionA Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.

In the application of parallel kinematic machine tools (PKM), because of errors in the geometric parameters, it is necessary to calibrate the PKM to improve the positioning accuracy. In existing self-calibration methods, either some redundant sensors on passive joints or some mobility constraints on the kinematic chains are used. However, the mobility constraints imposed on kinematic chains might apply large forces during the test on legs and passive joints. Also, these kinds of calibrating are applicable only on PKMs in which their actuated joints can be used as passive joints. To overcome weaknesses of existing methods, a novel approach to calibration based on imposing position constraints on the tool center point (TCP) is introduced. In this method, only the data of encoders installed on the actuated joints in some sets of configurations are required. In each set, the position of the TCP is fixed, but orientations of the tool are different. Simulations and experimental studies on a Hexaglide PKM built in Sharif University of Technology reveal the convenience and effectiveness of the proposed robot calibration method.

This paper presents the implementation of an novel inertial localization system for 3D positioning including 3 main functions: unscented Kalman filter (UKF) based sensor data fusion for accurate orientation determination; magnetic disturbance detection and minimization for robustness and stability when experiencing local magnetic disturbance; Zero Velocity Update (ZUPT) for velocity correction. In both indoor and outdoor experiment scenarios, the system is able to obtain correct 3D trajectory.

Given the relevance of sensorless control of surface-mounted permanent magnet synchronous machines (SPMSM) and the challenges of estimating their position and speed with non- sinusoidal back-electromotive force (back-EMF), this work aims to study sensorless methods for medium and high speeds and suggests alternatives to the existing techniques in the literature for this type of machine. Two new approaches are proposed: the first is based on the classic Kalman filter and infers the machines rotor position from the back-EMF estimation in the αβ reference frame; and the second, for rotor speed estimation, consists of a new adaptation law, calculated in the dqx reference frame, for reference model adaptive system (MRAS) estimation. Both proposed methods consider the non-sinusoidal characteristic of the back-EMF and are suitable for any SPMSM. The MRAS-based solution is also appropriate for interior permanent magnet synchronous machines. The vector control of the SPMSM is implemented in the dqx reference frame. The experimental results compared the proposed methods, briefly called KF (proposed) and MRAS dqx, with three others already presented in the literature: extended Kalman filter (called EKF) for direct position and speed estimation; classical Kalman filter for back-EMF estimation ignoring back-EMF dynamics (KF (e = 0)); and classical Kalman filter for back-EMF estimation ignoring back-EMF harmonic components other than the fundamental (KF (sinusoidal)). KF (e = 0), KF (sinusoidal), and KF (proposed) concurrently use the proposed MRAS-based method for speed estimation. The five approaches are compared considering transient (step commands for speed variation and reversal) and steady-state responses (speeds from 20 rad/s to 140 rad/s in steps of 20 rad/s, with and without load torque). The minimum operating speed at no load and the execution time for each method are also checked. All the techniques show similar performance about measured and estimated speed dynamics in transient tests, as well as similar minimum operating speeds. However, only EKF and MRAS dqx execute the reversal command and successfully keep the system stable below the minimum operating speed. As for the steady-state tests, KF (proposed) shows excellent position estimation in the speed range between 20 rad/s and 80 rad/s, which results in low torque ripples. MRAS dqx shows excellent results above 100 rad/s, with the lowest position estimation errors and torque ripple. In terms of computational burden, MRAS dqx has the lowest execution time, and KF (proposed) comes in third, with a time just longer than KF (e = 0). Therefore, the experimental results demonstrate the effectiveness and usefulness of the proposed methods in the sensorless control of SPMSM with non-sinusoidal back-EMF, as well as advantages over other existing and consolidated techniques in the literature.

The inertial measurement system based on the SERF effect utilizes the rotation of the inertia-sensitive space with longitudinally hyperpolarized nuclear spin, possessing the potential for ultra-high sensitivity measurement, and has become an important development direction for the high-sensitivity miniaturized inertial navigation systems. The polarization distribution uniformity in the atomic spin ensemble significantly influences the scale factor error and bias stability of the SERF inertial measurement system. This paper analyses the factors affecting the longitudinal and transverse polarization distribution in the atomic spin coupled ensemble of the SERF inertial measurement system. To address the issue of non-uniform polarization rate distribution in the electron spin-nuclear spin coupled ensemble, a Bloch differential equation model is established. Using light field manipulation techniques, three-dimensional polarization rate distribution of the atomic spin ensemble under Gaussian, flat-top, and hollow beam pumping are simulated. Relevant simulation parameters are optimized, with over 80% of the normalized nuclear spin polarization rate considered as the uniform region. Under hollow beam pumping, the transverse nuclear spin polarization uniformity is improved by 88.3% compared to Gaussian beams and by 20.9% compared to flat-top beams, providing a theoretical basis for further enhancing the polarization uniformity in the atomic spin coupled ensemble.

Limited by the principle of multi-angle intersection measurement, photoelectric scanning measurement systems such as workshop Measuring and Positioning System (wMPS) and indoor Global Positioning System (iGPS) have systematical positioning errors when measuring moving targets. Fusing target movement information can effectively compensate the positioning error introduced by the relative motion between the target and the transmitter. Inertial measurement systems are capable of high-frequency, all-weather and autonomous rotation and acceleration measurement. They have good measurement complementarity with photoelectric scanning measurement systems. Integrating movement information from inertial measurement unit (IMU) is an effective method to improve the dynamic measurement performance. Inertial information fusion needs two preconditions: the first one is clock synchronization and the second one is spatial alignment. In response to the two problems above, we research on wMPS and propose a wMPS-IMU integrated online calibration method. The time offset of the two systems is precisely calibrated by compensating the delay between the wMPS reference clock and the IMU clock. Due to the fact that wMPS receivers and the IMU are connected as a rigid body, the rotation R and the translation T between the wMPS and the IMU can be easily calibrated through a linear estimation method. Our novel method benefits to achieve real-time online temporal and spatial calibration without the help of any external measurement equipment and prior information and significantly improves the applicability of wMPS. Verification experiments were carried out. A three-axis turntable and a linear guide were utilized as the roundness and straightness references. The calibration experiment shows that the calibration accuracy of our method is at the same level as that achieved from the high accuracy coordinate measuring machine (CMM). The precision evaluation experiment shows that the hybrid wMPS-IMU system calibrated through the proposed method has slightly better dynamic performance than that calibrated on a CMM. This method is fully verified to be effective.

The electric motor is a device that converts electrical power into mechanical power. The DC motor has some advantages compared to the AC motor on its easier way to control the speed or position and wide adjustable range. However, it still has some issues, such as uncertainty and disturbance; most existing controllers still provide bad system performance or have major drawbacks in solving these issues. The alternative solution for a control method with simple, easy to design, and can be implemented in a multi-input multi-output system is the integral state feedback control technique, such as in Linear Quadratic Regulator (LQR). The weakness of LQR control is that there is no exact method to determine the best value of matrix Q and R used to calculate the state feedback gain; trial or manual tuning method is mostly used. Therefore, this paper proposed an improved LQR control using Particle Swarm Optimization (PSO) method and the Kalman filter estimator. The PSO was used to find optimal Q and R-value, while the Kalman filter reduced the number of sensors used to measure the system state. Both performance and energy consumption were compared using the Integral of Absolute Error (IAE) and total energy consumption. The simulation results showed that the proposed method is superior in performance and energy compared to manually-tuned LQR. However, based on the hardware implementation, although the manually-tuned LQR was found to have the smallest energy consumption, the proposed method has the smallest IAE, which can reduce the IAE by 11.28% with only 1% higher energy compared to manually-tuned LQR.

System identification and tracking using a statistical model and a Kalman filter

Combined impulse-response/Kalman filtering (CIRKF) for input/state estimation

This paper presents a scheme for fault diagnosis in a flight control actuation system. The electromechanical control actuator considered here is based on a DC torque motor. The scheme utilizes the analytical redundancy that exists in the system between the linear actuator position, motor shaft angular velocity and motor current for diagnosis of incipient sensor faults in the system. Fault diagnosis is done using a single Kalman filter, which is driven by the motor shaft velocity sensor. Diagnosis of bias and scale factor faults in the position and current sensor is carried out using structured residuals that are generated using the Kalman filter estimates. Bias and scale factor errors in the velocity sensor are detected using the statistical properties of the innovation sequence of the Kalman filter. In the event of fault in the position sensor, real time reconfiguration using an estimate of the position from the Kalman filter is used for continued operation of the system. Robustness of the scheme to parameter variations is also examined