2,456 research outputs found
Inverse Optimal Planning for Air Traffic Control
We envision a system that concisely describes the rules of air traffic
control, assists human operators and supports dense autonomous air traffic
around commercial airports. We develop a method to learn the rules of air
traffic control from real data as a cost function via maximum entropy inverse
reinforcement learning. This cost function is used as a penalty for a
search-based motion planning method that discretizes both the control and the
state space. We illustrate the methodology by showing that our approach can
learn to imitate the airport arrival routes and separation rules of dense
commercial air traffic. The resulting trajectories are shown to be safe,
feasible, and efficient
Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients
In this paper, the multi-objective, multifidelity optimization of a wing fence on an unmanned aerial vehicle (UAV) near stall is presented. The UAV under consideration is characterized by a blended wing body (BWB), which increases its efficiency, and a tailless design, which leads to a swept wing to ensure longitudinal static stability. The consequence is a possible appearance of a nose-up moment, loss of lift initiating at the tips, and reduced controllability during landing, commonly referred to as tip stall. A possible solution to counter this phenomenon is wing fences: planes placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary layer flow. These are optimized to obtain the design that would fence off the appearance of a pitch-up moment at high angles of attack, without a significant loss of lift and controllability. This brings forth a constrained multi-objective optimization problem. The evaluations are performed through unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations. However, since controllability cannot be directly assessed through computational fluid dynamics (CFD), surrogate-derived gradients are used. An efficient global optimization framework is developed employing surrogate modeling, namely regressive co-Kriging, updated using a multi-objective formulation of the expected improvement. The result is a wing fence design that extends the flight envelope of the aircraft, obtained with a feasible computational budget
Workshop on Verification and Theorem Proving for Continuous Systems (NetCA Workshop 2005)
Oxford, UK, 26 August 200
A Route Selection Problem Applied to Auto-Piloted Aircraft Tugs
The antithetical needs of increasing the air traffic, reducing the air pollutant and noise emissions, jointly
with the prominent problem of airport congestion spur to radically innovate the entire ground operation system
and airport management. In this scenario, an alternative autonomous system for engine-off taxiing (dispatch towing)
attracts the interest of the civil aviation world. Even though structural and regulatory limitations undermine
the employment of the already existing push-back tractors to this purpose, they remain the main candidates to
accomplish the mission. New technologies are already under investigation to optimize towbarless tractor joints,
so as to respond to the structure safety requirements. However, regulation limitations will soon be an issue. In
this paper, a software solution for a route selection problem in a discretized airport environment is presented, in
the believe that a full-authority control system, including tractors’ selection logic, path planning and mission event
sequencing algorithms will possibly meet the regulation requirements. Four different algorithms are implemented
and compared: two Hopfield-type neural networks and two algorithms based on graph theory. They compute the
shortest path, accounting for restricted airport areas, preferential directions and dynamic obstacles. The computed
route checkpoints serve as a reference for the tractor autopilot. Two different missions are analyzed, concerning
the towing of departing and arriving aircraft respectively. A single mission consists of three different events, called
phases: Phase 1 goes from the actual tractor position (eventually the parking zone) to the selected aircraft (parked
or just landed); Phase 2 is the actual engine-off taxi towing; Phase 3 is the tractor return to its own parking zone.
Both missions are simulated and results are reported and compared
Linear Hamilton Jacobi Bellman Equations in High Dimensions
The Hamilton Jacobi Bellman Equation (HJB) provides the globally optimal
solution to large classes of control problems. Unfortunately, this generality
comes at a price, the calculation of such solutions is typically intractible
for systems with more than moderate state space size due to the curse of
dimensionality. This work combines recent results in the structure of the HJB,
and its reduction to a linear Partial Differential Equation (PDE), with methods
based on low rank tensor representations, known as a separated representations,
to address the curse of dimensionality. The result is an algorithm to solve
optimal control problems which scales linearly with the number of states in a
system, and is applicable to systems that are nonlinear with stochastic forcing
in finite-horizon, average cost, and first-exit settings. The method is
demonstrated on inverted pendulum, VTOL aircraft, and quadcopter models, with
system dimension two, six, and twelve respectively.Comment: 8 pages. Accepted to CDC 201
Extending Mission Duration of UAS Multicopters: Multi-disciplinary Approach
Multicopters are important tools in industry, the military, and research but suffer from short flight times and mission durations. In this thesis, we discuss three different ways to increase flight times and therefore increase the viability of using multicopters in a variety of missions. Alternate fuel sources such as hydrogen fuel and solar cells are starting to be used on multicopters, in our research we simulate modern fuel cells and show how well they currently work as the power source for multicopters and how close they are to becoming useful in Unmanned Aircraft System (UAS) technology. Increasing the efficiency in which the available energy is used can also increase mission duration. Two characteristics that affect the efficiency of a mission are the flight speeds of the multicopter and the payload it carries. These characteristics are well known in larger rotorcrafts but often ignored in smaller multicopters. In our research, we explore the effect of flight speed on the dynamics of a multicopter and show that higher speeds lead to higher flight times due to the effect of translational lift. Lastly, we developed an online updating multi-flight planning algorithm for stop and charge missions, a method that can potentially indefinitely extend a mission. The multi-flight planning algorithm, the variable resolution horizon, reduces the computing resources necessary to 15% to 40% of a typical optimal planner while having a maximum 5.6% decrease in expected future reward, a metric for accuracy. The results of this thesis help guide decisions in fuel type for multicopter missions show examples of how to increase flight time through increasing efficiency and develop the framework for multi-flight missions.
Advisers: Justin Bradley and Carrick Detweile
Numerical optimal control with applications in aerospace
This thesis explores various computational aspects of solving nonlinear, continuous-time dynamic optimization problems (DOPs) numerically. Firstly, a direct transcription method for solving DOPs is proposed, named the integrated residual method (IRM). Instead of forcing the dynamic constraints to be satisfied only at a selected number of points as in direct collocation, this new approach alternates between minimizing and constraining the squared norm of the dynamic constraint residuals integrated along the whole solution trajectories. The method is capable of obtaining solutions of higher accuracy for the same mesh compared to direct collocation methods, enabling a flexible trade-off between solution accuracy and optimality, and providing reliable solutions for challenging problems, including those with singular arcs and high-index differential-algebraic equations.
A number of techniques have also been proposed in this work for efficient numerical solution of large scale and challenging DOPs. A general approach for direct implementation of rate constraints on the discretization mesh is proposed. Unlike conventional approaches that may lead to singular control arcs, the solution of this on-mesh implementation has better numerical properties, while achieving computational speedups. Another development is related to the handling of inactive constraints, which do not contribute to the solution of DOPs, but increase the problem size and burden the numerical computations. A strategy to systematically remove the inactive and redundant constraints under a mesh refinement framework is proposed.
The last part of this work focuses on the use of DOPs in aerospace applications, with a number of topics studied. Using example scenarios of intercontinental flights, the benefits of formulating DOPs directly according to problem specifications are demonstrated, with notable savings in fuel usage. The numerical challenges with direct collocation are also identified, with the IRM obtaining solutions of higher accuracy, and at the same time suppressing the singular arc fluctuations.Open Acces
Hermite regularization of the Lattice Boltzmann Method for open source computational aeroacoustics
The lattice Boltzmann method (LBM) is emerging as a powerful engineering tool
for aeroacoustic computations. However, the LBM has been shown to present
accuracy and stability issues in the medium-low Mach number range, that is of
interest for aeroacoustic applications. Several solutions have been proposed
but often are too computationally expensive, do not retain the simplicity and
the advantages typical of the LBM, or are not described well enough to be
usable by the community due to proprietary software policies. We propose to use
an original regularized collision operator, based on the expansion in Hermite
polynomials, that greatly improves the accuracy and stability of the LBM
without altering significantly its algorithm. The regularized LBM can be easily
coupled with both non-reflective boundary conditions and a multi-level grid
strategy, essential ingredients for aeroacoustic simulations. Excellent
agreement was found between our approach and both experimental and numerical
data on two different benchmarks: the laminar, unsteady flow past a 2D cylinder
and the 3D turbulent jet. Finally, most of the aeroacoustic computations with
LBM have been done with commercial softwares, while here the entire theoretical
framework is implemented on top of an open source library (Palabos).Comment: 34 pages, 12 figures, The Journal of the Acoustical Society of
America (in press
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