Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Reduction And Error Analysis For The Physical Sciences

Turbulence, Coherent Structures, Dynamical Systems and SymmetryP. Holmes, J. L. Lumley, G. Berkooz, and C. W. Rowley, 2nd ed., Cambridge University Press, Cambridge, England, U.K., 2012, 386 pp., $90

Control Systems Engineering

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Introduction To Infinite Dimensional Linear Systems Theory

https://repository.tudelft.nl/islandora/object/uuid%3Af2b6bdbd-5d11-4ec5-ac7e-d2c2ce21144c/datastream/OBJ/download

Brief paper: A new approach to linear regression with multivariate splines

Air data sensor fault detection and diagnosis is important for the safety of aircraft. In this paper, first an extension of the robust three-step Kalman filter to nonlinear systems is made by proposing a robust three-step unscented Kalman filter. The robust three-step unscented Kalman filter is found to be sensitive to the initial condition error when dealing with air data sensor fault estimation. A theoretical analysis of this sensitivity is presented and a novel adaptive three-step unscented Kalman filter is proposed which is able to cope with not only the estimation of the air data sensor faults, but also the detection and isolation of faults. The adaptive three-step unscented Kalman filter contains three steps: time update, fault estimation and measurement update. This approach can reduce the sensitivity to the initial condition error. Finally, the air data sensor fault detection and diagnosis performance of the adaptive three-step unscented Kalman filter is validated using simulated aircraft data. Ad...

/pdf/adaptive-three-step-kalman-filter-for-air-data-sensor-fault-5c8cearver.pdf

Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis

This paper presents an approach to the system identification of the Delfly II Flapping Wing Micro Air Vehicle (FWMAV) using flight test data. It aims at providing simple FWMAV aerodynamic models that can be used in simulations as well as in nonlinear flight control systems. The undertaken methodology builds on normal aircraft system identification methods and extends these with techniques that are specific to FWMAV model identification. The entire aircraft model identification cycle is discussed covering the set-up and automatic execution of the flight test experiments, the aircraft states, the aerodynamic forces and moments' reconstruction, the aerodynamic model structure selection, the parameter estimation and finally, the model validation. In particular, a motion capturing facility was used to record the flapper's position in time and from there compute the states and aerodynamic forces and moments that acted on it, assuming flap-averaged dynamics and linear aerodynamic model structures. It is shown th...

https://journals.sagepub.com/doi/pdf/10.1260/1756-8293.5.4.273

Linear Aerodynamic Model Identification of a Flapping Wing MAV Based on Flight Test Data

The research presented in this paper focuses on the effects of structural failures on the safe flight envelope of an aircraft, which is the set of all the states in which safe maneuver of the aircr...

/pdf/effects-of-structural-failure-on-the-safe-flight-envelope-of-52i6yij7jw.pdf

Effects of Structural Failure on the Safe Flight Envelope of Aircraft

Recently, an incremental type sensor based backstepping (SBB) control approach, based on singular perturbation theory and Tikhonov’s theorem, has been proposed. This Lyapunov function based method uses measurements of control variables and less model knowledge, and it is not susceptible to the model uncertainty caused by fault scenarios. In this paper, the SBB method has been implemented on a fixed wing aircraft with its focus on handling structural changes caused by damages. A new hybrid autopilot flight controller has been developed for a Boeing 747-200 aircraft after combining nonlinear dynamic inversion (NDI) with SBB control approach. Two benchmarks for fault tolerant flight control (FTFC), named rudder runaway and engine separation, are employed to evaluate the proposed method. The simulation results show that the proposed control approach leads to a zero tracking-error performance in nominal condition and guarantees the stability of the closed-loop system under failures as long as the reference commands are located in the safe flight envelope.

/pdf/a-hybrid-sensor-based-backstepping-control-approach-with-its-2w0n8qi9yl.pdf

C.C. de Visser

Papers

Brief paper: A new approach to linear regression with multivariate splines

Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis

Linear Aerodynamic Model Identification of a Flapping Wing MAV Based on Flight Test Data

Effects of Structural Failure on the Safe Flight Envelope of Aircraft

A Hybrid Sensor Based Backstepping Control Approach with its Application to Fault-Tolerant Flight Control