Decision-making for unmanned aerial vehicle operation in icing conditions

Abstract

With the increased use of unmanned aerial systems (UAS) for civil and commercial applications, there is a strong demand for new regulations and technology that will eventually permit for the integration of UAS in unsegregated airspace. This requires new technology to ensure sufficient safety and a smooth integration process. The absence of a pilot on board a vehicle introduces new problems that do not arise in manned flight. One challenging and safety-critical issue is flight in known icing conditions. Whereas in manned flight, dealing with icing is left to the pilot and his appraisal of the situation at hand; in unmanned flight, this is no longer an option and new solutions are required. To address this, an icing-related decision-making system (IRDMS) is proposed. The system quantifies in-flight icing based on changes in aircraft performance and measurements of environmental properties, and evaluates what the effects on the aircraft are. Based on this, it determines whether the aircraft can proceed, and whether and which available icing protection systems should be activated. In this way, advice on an appropriate response is given to the operator on the ground, to ensure safe continuation of the flight and avoid possible accidents.

Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers

“…The set of conditions given by (45) has been verified using 256 gridding points, which correspond to the partition of each interval of variation of the state variables (u ∈ [16,21], w ∈ [0.5, 2.5], q ∈ [ −0.003, 0.003], ∈ [ −0.1, 0.3]) in four subintervals.…”

“…T( ) can be obtained from T( ) and T( ) by differentiating each element with respect to time. * It can be seen that the estimated bounds calculated as (15) and (18) are an appropriate combination of the proportional action given by (13) and (16) and the integral action given by (14) and (17).…”

“…∞ , the matrix R( ) be invertible, and the proportional integral interval UIO be given by (13)- (14), (16)- (17), and (41)- (44). Then, if there exist matrix functions K( ), K( ), T( ), T( ) ∈ R n x ×n x such that F( ), F( ), defined as in (19)- (20) are Metzler, relation (10) is satisfied provided that (11)- (12) hold.…”

“…∞ , the matrix function R( ) be such that (36) holds, and the proportional integral interval UIO be given by (13)- (14), (16)- (17), and (41)- (44). Then, if there exist matrix functions Γ( ), Γ( ) ∈ D n un ×n un and matrix functions S * ( ), S * ( ) ∈ R n x ×n x such that F( ), F( ), defined as in (19)- (20), with…”

“…The approaches recently applied to icing detection in aircrafts and UAVs include multiple models, 12,13 statistical methods, 14 aerodynamic coefficient estimators, 15 and environmental monitoring. 16 Unknown input observers (UIOs) are a special class of observers, which allow estimating the state of a system independently of some unknown inputs. 17 In addition, UIOs are a very useful tool for achieving a successful fault detection and isolation 18 because they can be made insensitive to certain input space directions if some structural algebraic conditions on the system are fulfilled.…”