Optimal control for minimizing power consumption during holding patterns for airborne wind energy pumping system

Licitra, Giovanni; Sieberling, Sören; Engelen, Steven; Williams, Paul; Ruiterkamp, Richard; Diehl, Moritz

doi:10.1109/ecc.2016.7810515

Cited by 11 publications

(12 citation statements)

References 21 publications

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“…The aircraft is subject to forces

{boldf}_{false{boldc, boldp, boldg false}}^{boldb}

and moments

{boldm}_{false{boldc, boldp, boldg false}}^{boldb}

coming from the c able, p ropellers and g ravity, whereas

{boldf}_{bolda}^{boldb} = {false[normalX, normalY, normalZ false]}^{⊤}

and

{boldm}_{bolda}^{boldb} = {false[normalL, normalM, normalN false]}^{⊤}

denote the aerodynamic forces and moments, respectively. The mathematical formulation in () is extensively used for pattern generation using an optimal control approach …”

Section: Modeling Of a Rigid‐wing Awesmentioning

confidence: 99%

“… The aerodynamic model (), () neglects the influence of parameter variation through time . One can account for such a model mismatch either by introducing a first‐order differential equation involving the angle of attack rate

\overset{α}{true}

or by designing flight trajectories customized for energy production that allow the aircraft to perform mild maneuvers The mathematical model () relies on Euler's equations, which describe the motion of rigid bodies only, hence flexible modes are implicitly neglected.…”

Section: Modeling Of a Rigid‐wing Awesmentioning

confidence: 99%

“…The mathematical formulation in (1) is extensively used for pattern generation using an optimal control approach. 20,21 In order to identify the aerodynamic forces f b a and moments m b a , one has to either discard or have good models of the other contributions. For this application, it is convenient to perform untethered flight tests to both simplify the overall system modeling and avoid disturbances caused by tether vibrations.…”

Section: Modeling Of Awes In Natural Coordinatesmentioning

confidence: 99%

“…] ∈ ℝ 12 (19) and control input equal to u(t) = e (t), (20) whereas the output states (13b) are simply given by…”

Section: Data Fittingmentioning

confidence: 99%

See 3 more Smart Citations

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Licitra

Bürger

Williams³

et al. 2019

Optim Control Appl Methods

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Summary Airborne wind energy (AWE) refers to a novel technology capable of harvesting energy from wind by flying crosswind patterns with tethered autonomous aircraft. Successful design of flight controllers for AWE systems relies on the availability of accurate mathematical models. Due to an expected nonconventional structure of the airborne component, the system identification procedure must be ultimately addressed via an intensive flight test campaign to gain additional insight about the aerodynamic properties. In this paper, the longitudinal dynamics of a rigid‐wing, high lift, autonomous aircraft for AWE are identified from experimental data obtained within flight tests. The aerodynamic characteristics are estimated via an efficient time‐domain multiple experiments model‐based parameter estimation algorithm.

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“…The aircraft is subject to forces

{boldf}_{false{boldc, boldp, boldg false}}^{boldb}

and moments

{boldm}_{false{boldc, boldp, boldg false}}^{boldb}

coming from the c able, p ropellers and g ravity, whereas

{boldf}_{bolda}^{boldb} = {false[normalX, normalY, normalZ false]}^{⊤}

and

{boldm}_{bolda}^{boldb} = {false[normalL, normalM, normalN false]}^{⊤}

denote the aerodynamic forces and moments, respectively. The mathematical formulation in () is extensively used for pattern generation using an optimal control approach …”

Section: Modeling Of a Rigid‐wing Awesmentioning

confidence: 99%

\overset{α}{true}

Section: Modeling Of a Rigid‐wing Awesmentioning

confidence: 99%

Section: Modeling Of Awes In Natural Coordinatesmentioning

confidence: 99%

“…] ∈ ℝ 12 (19) and control input equal to u(t) = e (t), (20) whereas the output states (13b) are simply given by…”

Section: Data Fittingmentioning

confidence: 99%

See 2 more Smart Citations

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Licitra

Bürger

Williams³

et al. 2019

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…A rigid wing AWES can be efficiently modeled as a set of Differential Algebraic Equations (DAEs) described by non-minimal coordinates by means of Lagrangian mechanics. The equations of motion for a tethered airborne component are given by [15]: (1) is extensively used for pattern generation using an optimal control approach [16,17].…”

Section: Modeling Of Awes In Natural Coordinatesmentioning

confidence: 99%

Viability assessment of a rigid wing airborne wind energy pumping system

Licitra¹,

Koenemann²,

Horn³

et al. 2017

2017 21st International Conference on Process Control (PC)

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Airborne Wind Energy (AWE) refers to a novel technology capable of harvesting energy from wind by flying crosswind patterns with tethered autonomous aircraft. Successful design of flight controllers for AWE systems rely on the availability of accurate mathematical models. Due to the non-conventional structure of the airborne component, the system identification procedure must be ultimately addressed via an intensive flight test campaign to gain additional insight about the aerodynamic properties. In this paper, aerodynamic coefficients are estimated from experimental data obtained within flight tests using an multiple experiments Model-Based Parameter Estimation (MBPE) algorithm.

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CasADi: a software framework for nonlinear optimization and optimal control

Andersson

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

2,600

976

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We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last seven years.

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Optimal control for minimizing power consumption during holding patterns for airborne wind energy pumping system

Cited by 11 publications

References 21 publications

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Viability assessment of a rigid wing airborne wind energy pumping system

CasADi: a software framework for nonlinear optimization and optimal control

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