Bayesian calibration for multiple source regression model

In large variety of practical applications, using information from different sources or different kind of data is a reasonable demand. The problem of studying multiple source data can be represented as a multi-task learning problem, and then the information from one source can help to study the information from the other source by extracting a shared common structure. From the other hand, parameter evaluations obtained from various sources can be confused and conflicting. This paper proposes a Bayesian based approach to calibrate data obtained from different sources and to solve nonlinear regression problem in the presence of heteroscedastisity of the multiple-source model. An efficient algorithm is developed for implementation. Using analytical and simulation studies, it is shown that the proposed Bayesian calibration improves the convergence rate of the algorithm and precision of the model. The theoretical results are supported by a synthetic example, and a real-world problem, namely, modeling unsteady pitching moment coefficient of aircraft, for which a recurrent neural network is constructed

Two-layer adaptive augmentation for incremental backstepping flight control of transport aircraft in uncertain conditions

Presence of uncertainties caused by unforeseen malfunctions in actuation system or changes in aircraft behaviour could lead to aircraft loss-of-control during flight. The paper presents Two-Layer Adaptive augmentation for Incremental Backstepping (TLA-IBKS) control algorithm designed for a large transport aircraft. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of control effectiveness. The proposed technique is capable to detect possible failures for an overactuated system. At the first layer, the system performs monitoring of a combined effectiveness and detects possible failures via an innovation process. If a problem is detected the algorithm initiates the second-layer algorithm for adaptation of effectiveness of individual control effectors. Filippov generalization for nonlinear differential equations with discontinuous right-hand sides is utilized to develop Lyapunov based tuning function adaptive law for the second layer adaptation and to prove uniform asymptotic stability of the resultant closed-loop system. Conducted simulation manifests that if the input-affine property of the IBKS is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the proposed TLA-IBKS algorithm demonstrates improved stability and tracking performance

Two-layer on-line parameter estimation for adaptive incremental backstepping flight control for a transport aircraft in uncertain conditions

Presence of uncertainties caused by unforeseen malfunctions of the actuator or changes in aircraft behavior could lead to aircraft loss of control during flight. The paper presents two-layer parameter estimation procedure augmenting Incremental Backstepping (IBKS) control algorithm designed for a large transport aircraft. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of the control effectiveness. The proposed identification technique is capable to detect possible problems such as a failure or presence of unknown actuator dynamics even in case of redundancy of control actuation. At the first layer, the system performs monitoring of possible failures. If a problem in one of the control direction is detected the algorithm initiates the second-layer identification determining the individual effectiveness of the each control surface involved in this control direction. Analysis revealed a high robustness of the IBKS to actuator failures. However, in severe conditions with a combination of multiple failures and presence of unmodelled actuator dynamics IBKS could lost stability. Meanwhile, proposed control derivative estimation procedure augmenting the IBKS control helps to sustain stability

Estimation of non-symmetric and unbounded region of attraction using shifted shape function and R-composition

A general numerical method using sum of squares programming is proposed to address the problem of estimating the region of attraction (ROA) of an asymptotically stable equilibrium point of a nonlinear polynomial system. The method is based on Lyapunov theory, and a shape function is defined to enlarge the provable subset of a local Lyapunov function. In contrast with existing methods with a shape function centered at the equilibrium point, the proposed method utilizes a shifted shape function (SSF) with its center shifted iteratively towards the boundary of the newly obtained invariant subset to improve ROA estimation. A set of shifting centers with corresponding SSFs is generated to produce proven subsets of the exact ROA and then a composition method, namely R-composition, is employed to express these independent sets in a compact form by just a single but richer-shaped level set. The proposed method denoted as RcomSSF brings a significant improvement for general ROA estimation problems, especially for non-symmetric or unbounded ROA, while keeping the computational burden at a reasonable level. Its effectiveness and advantages are demonstrated by several benchmark examples from literature.Comment: 40 pages, 9 figure

Flettner-rotor-powered VTOL’s theoretical performances

The demand for VTOLs is growing rapidly. From inspection to logistics, thanks to the evolution of autonomous technology. However, most of the fields remain just possibilities rather than actual usage cases due to the limited flight range, wall interaction, noise and instability of the aircraft. Alleviating an instability is especially an important key to achieve a reliable aerial system. One reason of instability is due to the limited four degrees of freedom. (i.e. VTOLs have to tilt to move horizontally) In this paper, a Flettner-force-powered VTOL that has sixdegrees of freedom, called Horizonist, is proposed and the stability of Horizonist is discussed in comparison to the conventional. (Horizonist is filed as a patent in Japan (issued as JP6938005) and PCT) The performances are evaluated in terms of settling time and power consumption against various inputs and a wind

Parametric analysis of battery-electric rotorcraft configurations to fly on Mars

The success of NASA’s Ingenuity Helicopter promises that the future exploration of Mars will include aerobots in line with rovers and landers. However, Ingenuity lacks long-range endurance and scientific payload capacity because of its small and elementary design. In the series of optimised Martian drone concepts development, we introduced in this paper - an initial sizing of rotary eVTOL design configurations based on the performed parametric analysis for hover and vertical climb using simplified rotorcraft momentum theory, for a set of more challenging requirements for a Martian aerobot mission and sized to fit into the maximum spacecraft aeroshell limit. A tandem rotor configuration was found to be the most efficient configuration, whereas a conventional single main rotor configuration with small diameters manifested the poorest performance

Fault detection in aircraft flight control actuators using support vector machines

Future generations of flight control systems, such as those for unmanned autonomous vehicles (UAVs), are likely to be more adaptive and intelligent to cope with the extra safety and reliability requirements due to pilotless operations. An efficient fault detection and isolation (FDI) system is paramount and should be capable of monitoring the health status of an aircraft. Historically, hardware redundancy techniques have been used to detect faults. However, duplicating the actuators in an UAV is not ideal due to the high cost and large mass of additional components. Fortunately, aircraft actuator faults can also be detected using analytical redundancy techniques. In this study, a data-driven algorithm using Support Vector Machine (SVM) is designed. The aircraft actuator fault investigated is the loss-of-effectiveness (LOE) fault. The aim of the fault detection algorithm is to classify the feature vector data into a nominal or faulty class based on the health of the actuator. The results show that the SVM algorithm detects the LOE fault almost instantly, with an average accuracy of 99%

Predicting autonomous vehicle navigation parameters via image and image-and-point cloud fusion-based end-to-end methods

This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. Image-based and Image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained Image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed