325 research outputs found
Predictive Control of Autonomous Kites in Tow Test Experiments
In this paper we present a model-based control approach for autonomous flight
of kites for wind power generation. Predictive models are considered to
compensate for delay in the kite dynamics. We apply Model Predictive Control
(MPC), with the objective of guiding the kite to follow a figure-of-eight
trajectory, in the outer loop of a two level control cascade. The tracking
capabilities of the inner-loop controller depend on the operating conditions
and are assessed via a frequency domain robustness analysis. We take the
limitations of the inner tracking controller into account by encoding them as
optimisation constraints in the outer MPC. The method is validated on a kite
system in tow test experiments.Comment: The paper has been accepted for publication in the IEEE Control
Systems Letters and is subject to IEEE Control Systems Society copyright.
Upon publication, the copy of record will be available at
http://ieeexplore.ieee.or
State Estimation for Kite Power Systems with Delayed Sensor Measurements
We present a novel estimation approach for airborne wind energy systems with ground-based control and energy generation. The estimator fuses measurements from an inertial measurement unit attached to a tethered wing and position measurements from a camera as well as line angle sensors in an unscented Kalman filter. We have developed a novel kinematic description for tethered wings to specifically address tether dynamics. The presented approach simultaneously estimates feedback variables for a flight controller as well as model parameters, such as a time-varying delay. We demonstrate the performance of the estimator for experimental flight data and compare it to a state-of-the-art estimator based on inertial measurements
Multi-Agent Goal Assignment with Finite-Time Path Planning
Minimising the longest travel distance for a group of mobile robots with
interchangeable goals requires knowledge of the shortest length paths between
all robots and goal destinations. Determining the exact length of the shortest
paths in an environment with obstacles is challenging and cannot be guaranteed
in a finite time. We propose an algorithm in which the accuracy of the path
planning is iteratively increased. The approach provides a certificate when the
uncertainties on estimates of the shortest paths become small enough to
guarantee the optimality of the goal assignment. To this end, we apply results
from assignment sensitivity assuming upper and lower bounds on the length of
the shortest paths. We then provide polynomial-time methods to find such bounds
by applying sampling-based path planning. The upper bounds are given by
feasible paths, the lower bounds are obtained by expanding the sample set and
leveraging knowledge of the sample dispersion. We demonstrate the application
of the proposed method with a multi-robot path-planning case study
Auction algorithm sensitivity for multi-robot task allocation
We consider the problem of finding a low-cost allocation and ordering of
tasks between a team of robots in a d-dimensional, uncertain, landscape, and
the sensitivity of this solution to changes in the cost function. Various
algorithms have been shown to give a 2-approximation to the MinSum allocation
problem. By analysing such an auction algorithm, we obtain intervals on each
cost, such that any fluctuation of the costs within these intervals will result
in the auction algorithm outputting the same solution
Uncertainty Intervals for Robust Bottleneck Assignment
We examine the robustness of bottleneck assignment problems to perturbations
in the assignment weights. We derive two algorithms that provide uncertainty
bounds for robust assignment. We prove that the bottleneck assignment is
guaranteed to be invariant to perturbations which lie within the provided
bounds. We apply the method to an example of task assignment for a multi-agent
system.Comment: 6 pages, 1 figure, accepted at the European Control Conferenc
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