Terminal Iterative Learning Control for Autonomous Aerial Refueling Under Aerodynamic Disturbances

Abstract

Aerial refueling has demonstrated significant benefits to aviation by extending the range and endurance of aircraft [1]. The development of autonomous aerial refueling (AAR) techniques for unmanned aerial vehicles (UAVs) makes new missions and capabilities possible [2], like the ability for long range or long time flight. As the most widely used aerial refueling method, the probedrogue refueling (PDR) system is considered to be more flexible and compact than other refueling systems. However, a drawback of PDR is that the drogue is passive and susceptible to aerodynamic disturbances [3]. Therefore, it is difficult to design an AAR system to control the probe on the receiver to capture the moving drogue within centimeter level in the docking stage.It used to be thought that the aerodynamic disturbances in the aerial refueling mainly include the tanker vortex, wind gust, and atmospheric turbulence. According to NASA Autonomous Aerial Refueling Demonstration (AARD) project [2], the forebody flow field of the receiver may also significantly affect the docking control of AAR, which is called "the bow wave effect" [4]. As a result, the modeling and simulation methods for the bow wave effect were studied in our previous works [5,6].Since the obtained mathematical models are somewhat complex and there may be some uncertain factors in practice, this paper aims to use a model-free method to compensate for the docking error caused by aerodynamic disturbances including the bow wave effect.Most of the existing studies on AAR docking control do not consider the bow wave effect. In [7][8][9], the drogue is assumed to be relatively static (or oscillates around the equilibrium) and not affected by the flow field of the receiver forebody. However, in practice, the receiver aircraft is affected by aerodynamic disturbances, and the drogue is affected by both the wind disturbances and the receiver forebody bow wave. As a major difficulty in the control of AAR, the aerodynamic disturbances, especially the bow wave effect, attract increasing attention in these years. In [10,11], the wind effects from the tanker vortex, the wind gust, and the atmospheric turbulence are analyzed, and in [5,6,12], the modeling and simulation methods for the receiver forebody bow wave effect are studied, but no control methods are proposed. In [4], simulations show that the bow wave effect can be compensated by adding an offset value to the reference trajectory, but the method for obtaining the offset value is not given.

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

“…4 and Table . 5, we summarize some of the recently developed ILC algorithms in equations applied to UAVs and the strategies of ILC for more generalized and specific tasks, respectively. It is shown from Table IV, that in a large class of practical systems, such as autonomous aerial refueling based on terminal ILC [149] and [150], it is required that the output achieves perfect tracking at more than one defined time instants t " t i respectively. Therefore, it needs an extension of terminal ILC to solve problems which only require tracking of a number of critical positions at a subset of time instants.…”

“…Therefore, it needs an extension of terminal ILC to solve problems which only require tracking of a number of critical positions at a subset of time instants. P-type u k`1 ptq " u k ptq`γe k ptq [138], [139] D-type u k`1 ptq " u k ptq`α 9 e k ptq [138], [148] PID-type u k`1 ptq " u k ptq`γe k ptq`α 9 e k ptq + β ż t 0 e k psqds [139], [141], [147], [150] Adjoint-type u k`1 ptq " u k ptq´αη k ptq [147] , [148] Gradient-based u k`1 ptq " u k ptq`β k R´1G T Qe k ptq [141] Norm Optimal u k`1 ptq " u k ptq`G˚pI´GG˚q´1e k ptq [142] A range of ILC approaches were reviewed, including simple structure controllers for ILC which have been presented in discrete-time. Also, the above methods are limited in terms of the accuracy they have attained.…”

“…Content may change prior to final publication. [150] art battery material technologies, (ii) to charge the battery from an external source which can be either wired or wireless approaches. Our field of review will focus on the second option as wired charging is deemed impractical.…”

Iterative Learning in Functional Space for Non-Square Linear Systems

“…Another key difference is that the functional expansion is operated directly on the discrete system rather than as an alternative to zeroorder holding. In terminal ILC (TILC) [11]- [13] and Point-to-Point ILC (P2PILC) [14]- [16], the input signal is still a piecewise constant function but sampled with higher frequency w.r.t. the output.…”

Docking Controller for Autonomous Aerial Refueling With Adaptive Dynamic Surface Control

“…In [21], Z. Su et al utilize high order sliding mode observer (HOSMO) to achieve better estimation effects. Moreover, some modern control methods are also studied for their application in AAR control, such as: fault-tolerant control [22], [23] and terminal iterative learning control (TILC) [6], [24].…”