Examinando por Autor "Buelta Méndez, Almudena"

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A Gaussian Process Iterative Learning Control for Aircraft Trajectory Tracking
(IEEE, 2021-07-20) Buelta Méndez, Almudena; Olivares González, Alberto; Staffetti Giammaria, Ernesto; Aftab, Waqas; Mihaylova, Lyudmila
This article proposes a recursive Gaussian process regression with a joint optimization-based iterative learning control algorithm to estimate and predict disturbances and model uncertainties affecting a flight. The algorithm proactively compensates for the predicted disturbances, improving precision in aircraft trajectory tracking. Higher precision in trajectory tracking implies an improvement of the aircraft trajectory predictability and, therefore, of the air traffic management system efficiency. Airlines can also benefit from this higher predictability by reducing the number of alterations when following their designed trajectories, which entails a reduction of costs and emissions. The iterative learning control algorithm is divided into two steps: first, a recursive Gaussian process regression estimates and predicts perturbations and model errors with no need for prior knowledge about their dynamics and with low computational cost, and second, this information is used to update the control inputs so that the subsequent aircraft intending to fly the same planned trajectory will follow it with greater precision than the previous ones. This method is tested on a simulated commercial aircraft performing a continuous climb operation and compared to an iterative learning algorithm using a Kalman filter estimator in a similar scenario. The results show that the proposed approach provides 62 and 42% precision improvement in tracking the desired trajectory, as compared to the Kalman filter approach, in two experiments, where no prior knowledge of the un- modeled dynamics was available, also achieving it in less iterations.
Iterative Learning Control for Precise Aircraft Trajectory Tracking in Continuous Climb Operations
(Thirteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2019), 2019) Buelta Méndez, Almudena; Olivares González, Alberto; Staffetti Giammaria, Ernesto
In this paper, an iterative learning control method is used to improve precision in aircraft trajectory tracking in which, given a departure procedure, the dynamical model of an aircraft and a trajectory to be followed, the problem consists in defining an iterative learning control scheme which is able to improve the precision of the aircraft in following the trajectory taking into account the deviations suffered by previous flights. It is assumed that all the flights are operated with the same aircraft model and that they successively follow the same trajectory with short time-based separation and therefore are subject to similar recurrent disturbances. In the iterative learning control scheme used in this paper, the control action consists in generating at each iteration a new reference trajectory for the aircraft which compensates for recurrent disturbances. Thus, it can be applied to systems with underlying controllers for trajectory tracking, such as aircraft. In this case, the feedback trajectory tracking control is intended to reduce non-repetitive disturbances while the iterative learning control is intended to reject repetitive disturbances. The iterative learning control problem is solved in two steps: disturbance estimation and aircraft reference trajectory update. Both steps rely on a nominal model of the aircraft in which input and state constraints are explicitly taken into account. Continuous climb operations, defined within a standard instrumental departure, are considered in the simulations. The result show the effectiveness of the method which is able to reduce the trajectory tracking error due to recurrent disturbances in a few iterations, thus improving their predictability. Higher predictability of aircraft trajectories would simplify both management and control of air traffic, would improve the capacity of the air traffic management system and would allow a better exploitation of the infrastructures. Greater predictability of aircraft trajectories would also allow airlines to define and follow trajectories with a smaller number of alterations. This would result in a reduction of costs and emissions.
Towards Multi-Aircraft Transfer Learning for Trajectory Tracking
(Fifteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2023), 2023) Buelta Méndez, Almudena; Olivares González, Alberto; Staffetii Giammaria, Ernesto
In this paper, a control method that combines model reference adaptive control (MRAC) and iterative learning con- trol (ILC) is applied to aircraft trajectory tracking. ILC is intended for repetition-invariant system dynamics, in which the nominal dynamic model of the aircraft is assumed to be known. However, in real operations, this requirement is not met, as different aircraft perform consecutive flights along the same trajectory. To address this limitation, a multi-aircraft transfer learning strategy is proposed, which allows the learned trajectory knowledge to be transferred to dynamically different aircraft at each iteration. In order to achieve this, the aircraft’s baseline controller is augmented with an MRAC, which drives the system’s performance close to that of a reference model, and an ILC, which serves as a high-level adaptation scheme to compensate for repetitive disturbances. According to the preliminary results obtained from experiments carried out with various simulated aircraft, taking into account model uncer- tainties, disturbances, and changing dynamics, the performance of the ILC in combination with the MRAC augmentation of a baseline controller is superior to that of the baseline controller without MRAC augmentation. After a few iterations, a signifi- cant reduction in the trajectory tracking error is observed, with only small fluctuations occurring throughout the subsequent iterations. As a result, the MRAC- ILC combination makes the ILC applicable to real operations, improves the predictability of aircraft trajectories, and enhances the efficiency of the air traffic management system.