Examinando por Autor "Buelta, Almudena"

Mostrando 1 - 3 de 3

Chance-constrained stochastic optimal control of epidemic models: A fourth moment method-based reformulation
(Elsevier, 2024-12) Buelta, Almudena; Olivares, Alberto; Staffetti, Ernesto
This work proposes a methodology for the reformulation of chance-constrained stochastic optimal control problems that ensures reliable uncertainty management of epidemic outbreaks. Specifically, the chance constraints are reformulated in terms of the first four moments of the stochastic state variables through the so-called fourth moment method for reliability. Moreover, a spectral technique is employed to obtain surrogate models of the stochastic state variables, which enables the efficient computation of the required statistics. The practical implementation of the proposed approach is demonstrated via the optimal control of two different stochastic mathematical models of the COVID-19 transmission. The numerical experiments confirm that, unlike those reformulations based on the Chebyshev–Cantelli’s inequality, the proposed method does not exhibit the undesired outcomes that are typically observed when a higher precision is required for the risk level associated to the given chance constraints
Multi-Aircraft Transfer Learning for Aircraft Trajectory Tracking in Continuous Climb and Descent Operations
(Institute of Electrical and Electronics Engineers, 2024-06-03) Buelta, Almudena; Olivares, Alberto; Staffetti, Ernesto
This paper presents a control technique for aircraft trajectory tracking that combines Iterative Learning Control (ILC) with Model Reference Adaptive Control (MRAC). ILC enhances the accuracy of aircraft in following a predefined trajectory based on the deviations in space and time observed in previous flights to anticipate repetitive disturbances proactively. However, ILC requires the system to be repetition-invariant, which is not applicable in real operations where different aircraft consecutively perform flights along the same trajectory. To address this drawback, a multi-aircraft transfer learning strategy is considered. At each iteration of the ILC, this strategy allows learned trajectory knowledge to be transferred among different aircraft. The proposed approach involves augmenting the baseline aircraft trajectory tracking controller with an MRAC, which ensures that the aircraft behaves similarly to a given reference model, while the ILC acts as a high-level adaptation scheme, compensating for repetitive disturbances affecting the flight. Numerical experiments are conducted using various simulated aircraft following the same trajectory in continuous climb and descent operations. Results show that the MRAC-ILC method outperforms the combination of ILC with the baseline feedback controller without MRAC augmentation, achieving a substantial reduction in the trajectory tracking error after a few iterations. This improvement remains consistent even in the presence of model uncertainties, disturbances, and changing aircraft dynamics. In summary, the MRAC-ILC method makes ILC suitable for real operations, enhances the predictability of aircraft trajectories, and consequently improves the efficiency of the air traffic management system.
Planning and Control of Aircraft Ground Movement Operations with Towbarless Robotic Tractors
(Institute of Electrical and Electronics Engineers, 2024-07-18) Buelta, Almudena; Olivares, Alberto; Staffetti, Ernesto
This article studies the automation of aircraft ground movement operations using towbarless robotic tractors. The tractor-aircraft system is modeled as a car-like mobile robot with an off-hooked trailer, in which an accurate dynamic model of the tractor-aircraft system is employed. The primary objective of this study is to determine the control inputs and the resulting collision-free trajectories to steer the aircraft from the initial to the final position, under the assumption that the model of the system and the positions of the obstacles are known. This trajectory planning problem is formulated as an energy-time optimal control problem, which is solved using a pseudospectral knotting numerical method. The effects of the uncertainty in the weight of the aircraft on the solution of the planning problem are also quantified. Since, in general, the tractor-aircraft system moves backwards during ground movement operations, the issue of jackknifing is also addressed. Therefore, the secondary objective of this article is to deal with the problem of tracking the planned trajectory while preventing jackknifing. The trajectory tracking problem is solved using a Jacobian linearization of the offset dynamics about the planned trajectory, in which the optimal control inputs are used as feedforward terms to improve tracking precision