Examinando por Autor "Staffetti Giammaria, Ernesto"

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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 and Descent Operations
(IEEE, 2021-07-14) Buelta Mendez, Almudena; Olivares González, Alberto; Staffetti Giammaria, Ernesto
This paper presents an iterative learning control method for precise aircraft trajectory tracking. Given a trajectory to be followed by an aircraft with a dynamical model which is assumed to be known, the proposed algorithm improves the system performance in following the trajectory using the spatial and temporal deviations suffered by previous flights to anticipate recurring disturbances and compensate for them proactively by generating a new reference trajectory to be followed, which is the input for the aircraft’s own trajectory tracking controller. The proposed method is tested in a simulated busy terminal maneuvering area in which the time-based separation between aircraft is short enough for similar weather conditions to be expected. The numerical experiments are conducted considering aircraft of the same type, which are assumed to follow the same trajectory in two operations in which precise trajectory tracking is essential: continuous climb and descent operations. The obtained results show a significant reduction of the trajectory tracking error in few iterations, proving the effectiveness of the iterative learning control method applied to commercial aircraft trajectory tracking. Higher precision in trajectory tracking implies higher predictability of aircraft trajectories, which results in an improvement of the efficiency and capacity of the air traffic management system and in reductions of costs and emissions.
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.