Position and Attitude Control of Underactuated Drones Using the Adaptive Function Approximation Technique

Despite the popularity of drones and their relatively simple operation, the underlying control algorithms can be difficult to design due to the drones’ underactuation and highly nonlinear properties. This paper focuses on position and orientation control of drones to address challenges such as path and edge tracking, and disturbance rejection. The adaptive function approximation technique control method is used to control an underactuated and nonlinear drone. The controller utilizes reference attitude signals, that are derived from a proportional derivative (PD) linear feedback control methodology. To avoid analytic expressions for the reference attitude velocities, we employ a continuous-time Kalman filter based on a model of the measurement signal — which is derived by passing the reference attitude position through a low-pass signal differentiator — as a second-order Newtonian system. Stability of the closed loop system is proven using a Lyapunov function. Our design methodology simplifies the control process by requiring only a few tuning variables, while being robust to time-varying and time-invariant uncertainties with unknown variation bounds, and avoids the requirement for the knowledge of the dynamic equation that governs the attitude of the drone. Three different scenarios are simulated and our control method shows better accuracy than the proportional-derivative controller in terms of edge tracking and disturbance rejection.

Simultaneous Quantification of Protein Binding Kinetics in Whole Cells with Surface Plasmon Resonance Imaging and Edge Deformation Tracking

Most drugs work by binding to receptors on the cell surface. Quantification of binding kinetics between drug and membrane protein is an essential step in drug discovery. Current methods for measuring binding kinetics involve extracting the membrane protein and labeling, and both have issues. Surface plasmon resonance (SPR) imaging has been demonstrated for quantification of protein binding to cells with single-cell resolution, but it only senses the bottom of the cell and the signal diminishes with the molecule size. We have discovered that ligand binding to the cell surface is accompanied by a small cell membrane deformation, which can be used to measure the binding kinetics by tracking the cell edge deformation. Here, we report the first integration of SPR imaging and cell edge tracking methods in a single device, and we use lectin interaction as a model system to demonstrate the capability of the device. The integration enables the simultaneous collection of complementary information provided by both methods. Edge tracking provides the advantage of small molecule binding detection capability, while the SPR signal scales with the ligand mass and can quantify membrane protein density. The kinetic constants from the two methods were cross-validated and found to be in agreement at the single-cell level. The variation of observed rate constant between the two methods is about 0.009 s−1, which is about the same level as the cell-to-cell variations. This result confirms that both methods can be used to measure whole-cell binding kinetics, and the integration improves the reliability and capability of the measurement.

P892 Accuracy of right atrial pressure estimation using a multi-parameter approach derived from Inferior vena cava semi-automated edge-tracking echocardiography

Background In clinical practice, as stated in the ASE guidelines, the echocardiographic estimation of right atrial pressure (RAP) is based on the size of the inferior vena cava (IVC) and its inspiratory collapse. However, this method has proven to have limits of reliability and reproducibility. The use of a recently developed software that with a semi-automatic technique highlight the edges of the IVC could help to standardize the echocardiographic assessment of RAP. Aim of the study: The aim of the study was to assess feasibility and accuracy of a new semi-automated approach to estimate the RAP. Standard acquired echocardiographic images were processed with a semi-automatic technique, indexes related to the collapsibility of the vessel during inspiration (Caval Index, CI), during the whole respiratory cycle (Respiratory Caval Index, RCI) and through the heart cycle transmitted movements’ (Cardiac Caval Index (CCI) were derived (figure 1). Using these indexes, we developed two models: a) the Binary Tree Model (BTM), further divided in BTM3 and BTM5 (RAP estimated in 3 and 5 classes, respectively); b) the Regression Model (RM), further divided in RM linear (continuous model) and RM3 and RM5 (RAP estimated in 3 and 5 classes respectively). RAP assessed using these innovative techniques were compared with two standard estimation (SE) echocardiographic methods A and B. Direct RAP measurements obtained during a right heart catheterization (RHC), performed within 6 hours, were used as reference. Results 62 consecutive ‘all-comers’ patients that had a RHC were enrolled; 13 patients were excluded for technical reasons. Therefore 49 patients were included in this study (26 males and 23 females; mean age of 62.2 ± 15.2 years, 75.5% pulmonary hypertension, 34.7% severe left ventricular dysfunction and 51% right ventricular dysfunction). The two SE methods showed poor accuracy for RAP estimation (method A: ME = 51%, R2= 0.22; method B: ME = 69%, R2= 0.26). Instead, the new semi-automatic methods BTM3 and BTM5 based on parameters derived from IVC edge tracking (mean IVC diameter, CI, CCI and RCI) had a misclassification error of only 14% (R2 = 0.47) and 22% (R2 = 0.61), respectively, to classify RAP. The accuracy was lower for RM than BTM (RM3: ME = 61%, R2 = 0.39; RM5: ME = 55%, R2 = 0.39). However, the RM showed the lowest mean bias in estimating RAP: 0.23 [-8.34; 8.81] mmHg. Conclusions A multi-parametric approach using the new indexes, such as CCI and RCI, derived from a semi-automated edge tracking of the IVC is a promising tool for a more accurate estimation of RAP. This study proposes an innovative method for the non-invasive estimation of the RAP, which requires confirmation on larger population. Abstract P892 Figure 1

A novel engagement-pixel image edge tracking method for extracting gear tooth profile edge

Accurate gear profile plays an important role in determining the transmission performance of gear-drive equipment. In this paper, a novel method for extracting gear tooth profile edge is presented. The presented method is based on engagement-pixel image edge tracking (EPIET) technique, and does not rely on the traditional meshing theory. An algorithm for the proposed method is put forward. Firstly, instantaneous contact images between the envelope curves of the gear profile and the instantaneous locus of the cutting tool are acquired. Secondly, pixels on the boundary of the envelope curves are lighted and the instantaneous locus coordinates of the cutting tool are calibrated. Lastly, the pixel coordinates of instantaneous meshing points are extracted, based on the fact that there is exactly one contact point per instant, and major error sources of the presented method are discussed. To verify the effectiveness of the presented method, a case study is conducted on a face gear, which is a type of complex conjugate gear, to extract its tooth profile edge. In the study, the tooth profile error and the contact line error are investigated. The results demonstrate that the presented method, without knowing complicated meshing equations, can acquire feasible accuracy and stability, compared with traditional meshing equations. It is shown that the novel method can be extended to applications of digital design of complex conjugate curved surfaces, in a simple and fast manner.