Introduction to Adaptive Feedforward Control for Gust Loads Alleviation
Active control techniques for the gust loads alleviation/flutter suppression have been investigated extensively in the last decades to control the aeroelastic response, and improve the handling qualities of the aircraft. Nonadaptive feedback control algorithms such as classical single input single output techniques (Schmidt & Chen, 1986), linear quadratic regulator (LQR) theory (Mahesh et al., 1981; Newsom, 1979), eigenspace techniques (Garrard & Liebst, 1985; Leibst et al., 1988), optimal control algorithm (Woods-Vedeler et al., 1995), H∞ robust control synthesis technique (Barker et al., 1999) are efficient methods for the gust loads alleviation/flutter suppression. However, because of the time varying characteristics of the aircraft dynamics due to the varying configurations and operational parameters, such as fuel consumption, air density, velocity, air turbulence, it is difficult to synthesize a unique control law to work effectively throughout the whole flight envelope. Therefore, a gain scheduling technique is necessary to account for the time varying aircraft dynamics. An alternative methodology is the feedforward and/or feedback adaptive control algorithms by which the control law can be updated at every time step (Andrighettoni & Mantegazza, 1998; Eversman & Roy, 1996; Wildschek et al., 2006). With the novel development of the airborne LIght Detection and Ranging (LIDAR) turbulence sensor available for an accurate vertical gust velocity measurement at a considerable distance ahead of the aircraft (Schmitt, Pistner, Zeller, Diehl & Navé, 2007), it becomes feasible to design an adaptive feedforward control to alleviate the structural loads induced by any turbulence and extend the life of the structure. The adaptive feedforward control algorithm developed in (Wildschek et al., 2006) showed promising results for vibration suppression of the first wing bending mode. However, an unavoidable constraint for the application of this methodology is the usage of a high order Finite Impulse Response (FIR) filter. As a result, an overwhelming computation effort was needed to suppress the structural vibration of the aircraft. In this chapter, an adaptive feedforward control algorithm where the feedforward filter is parameterized using orthonormal basis expansions along with a recursive least square algorithm with a variable forgetting factor is proposed for the feedforward compensation of gust loads. With the use of the orthonormal basis expansion, the prior flexible modes information of the aircraft dynamics can be incorporated to build the structure of the feedforward controller. With this strategy, the order of the feedforward filter to be estimated can be largely reduced. As a result, the computation effort is greatly decreased, and the performance of the feedforward controller for gust loads alleviation will be enhanced. Furthermore, an FFT based PolyMAX identification method and the stabilization diagram program (Baldelli et al., 2009) are proposed to estimate the flexible modes of the aircraft dynamics. The need for an integrated model of flight dynamics and aeroelasticity is brought about by the emerging design requirements for slender, more flexible and/or sizable aircraft such as the Oblique Flying Wing (OFW), HALE, Sensorcraft and morphing vehicles, etc. Furthermore, a desirable unified nonlinear simulator should be formulated in principle by using commonly agreeable terms from both the flight dynamics and aeroelasticity fields in a consistent manner. A unified integration framework that blends flight dynamics and aeroelastic modeling approaches with wind-tunnel or flight-test data derived aerodynamic models has been developed in (Baldelli & Zeng, 2007). This framework considers innovative model updating techniques to upgrade the aerodynamic model with data coming from CFD/wind-tunnel tests for a rigid configuration or data estimated from actual flight tests when flexible configurations are considered. Closely following the unified integration framework developed in (Baldelli & Zeng, 2007), an F/A-18 Active Aeroelastic Wing (AAW) aeroelastic model with gust perturbation is developed in this chapter, and this F/A-18 AAW aeroelastic model can be implemented as a test-bed…
Introduction to Gain Tuning of Flight Control Laws for Satisfying Trajectory Tracking Requirements
The present chapter is concerned with presenting an approach for the synthesis of a gain- scheduled flight control law that assures compliance to trajectory tracking requirements. More precisely, a strategy is proposed for improving the tracking performances of a baseline controller, obtained by conventional synthesis techniques, by tuning its gains. The approach is specifically designed for atmospheric re-entry applications, in which gain scheduled flight control laws are typically used. Gain-scheduling design approaches conventionally construct a nonlinear controller by combining the members of an appropriate family of linear time-invariant (LTI) controllers (Leith & Leithead, 2000). The time-invariant feedback laws usually share the same structure, and differ only for the values of some tunable parameters, most notably the controller’s gains. These gains are generally determined taking advantage of well-assessed LTI-based design techniques, such as pole placement and gain/phase margin methods. However, once a set of LTI feedback laws is specified, the nonlinear controller must be synthesized, which requires an additional design step. This step is of considerable importance since the choice of nonlinear controller realization can greatly influence the closed loop performance (Leith…
Introduction to Quantitative Feedback Theory and Its Application in UAV’s Flight Control
Quantitative feedback theory (hereafter referred as QFT), developed by Isaac Horowitz (Horowitz, 1963; Horowitz and Sidi, 1972), is a frequency domain technique utilizing the Nichols chart in order to achieve a desired robust design over a specified region of plant uncertainty. Desired time-domain responses are transformed into frequency domain tolerances, which lead to bounds (or constraints) on the loop transmission function. The design process is highly transparent, allowing a designer to see what trade-offs are necessary to achieve a desired performance level. QFT is also a unified theory that emphasizes the use of feedback for achieving the desired system performance tolerances despite plant uncertainty and plant disturbances. QFT quantitatively formulates these two factors in the form of (a) the set R {TR } of acceptable D D command or tracking input-output relationships and the set {T } of acceptable disturbance input-output relationships, and (b) a set {P} of possible plants which…
Introduction to Fundamentals of GNSS-Aided Inertial Navigation
GNSS-aided inertial navigation is a core technology in aerospace applications from military to civilian. It is the product of a confluence of disciplines, from those in engineering to the geodetic sciences and it requires a familiarity with numerous concepts within each field in order for its application to be understood and used effectively. Aided inertial navigation systems require the use of kinematic, dynamic and stochastic modeling, combined with optimal estimation techniques to ascertain a vehicle’s navigation state (position, velocity and attitude). Moreover, these models are employed within different frames of reference, depending on the application. The goal of this chapter is to familiarize the reader with the relevant fundamental concepts. Background Modeling motion The goal of a navigation system is to determine the state of the vehicle’s trajectory in space relevant to guidance and control. These are namely its position, velocity and attitude at any time. In inertial navigation, a vehicle’s path is modeled kinematically rather than dynamically, as the full relationship of forces acting on the body to its motion is quite complex. The kinematic model incorporates accelerations and turn rates from an inertial measurement unit (IMU) and accounts for effects on the measurements of the reference frame in which the model is formalized. The kinematic model relies solely on measurements and known physical properties of the reference frame, without regard to vehicle dynamic characteristics.…
Introduction to ATM systems and Wind Farms
Air safety includes all the rules and processes that enable commercial and cargo aeroplanes to fly safely across the European Union. It includes rules on aircraft construction and use, infrastructure safety, data management and analysis, flying operations, and cargo. Air safety management aims to spot potential accidents and incidents before they occur. It is not the same as air security, which seeks to prevent voluntary illegal and harmful acts in the field of aviation. The wind is an increasingly important source of energy, but negative impact on air transport is in area of Air Traffic Services. Communication Navigation and Surveillance systems are endangered with big wind farms. Primary problem is in radar system and is detailed described in my text. The potential impacts of wind farms on air traffic management include the cumulative effects on the Slovak republic airspace management and surveillance infrastructure and affect the following systems: Primary Radar, Secondary Surveillance Radar (SSR), Microwave links associated with a) and …
Introduction to Legal aspects of Air traffic management based on satellite navigation
“Air Traffic Control’s primary objective is to ensure flight safety: pilots in their cockpit are to a large extent « blind » to the exterior world and, given the aircraft speed and trajectory complexity, it is necessary to control them from the ground in order to make sure that of course there are no accidents, but also to ensure the overall fluidity and efficiency of traffic flows. Air Traffic Control (ATC) is based on two main pillars: “surveillance”, which enables ground operators to know precisely where the aircraft are, and the “controller”, who manages the safety of flights .Ever since the implementation of radars in the 70s-80s as surveillance means, air traffic control has not evolved much: ATC is essentially “craftsmanship”, and relies entirely on the controllers’ individual capability to handle always more traffic. Even though air transport has exceptionally good reliability and safety records, to a large extent thanks to the high quality of work performed by air traffic controllers, this craftsmanship is becoming anachronistic: in the information society era, communications between controllers and pilots are still using the voice-radioiii!” The current Air Traffic Management (ATM) is based on ground navigational system such as radar and voice communications experience difficulty in meeting growing demand of air traffic. Despite economic recession ICAOiv expects moderate growth of air traffic of 3.3 percent to 5 percent during 2010-11v.According to aircraft manufacturer Airbus, global air passenger traffic is set to increase by over 150% over the next 20 years, representing an annual growth of 4.7%. The size of the world’s passenger aircraft fleet will double in number from 14,016 in 2008 to 28,111. The fastest growing regions will be India, China and Africa, driven by deregulation, economic growth, …
Introduction to Development of a Time-Space Diagram to Assist ATC in Monitoring
Continuous Descent Approaches Continuous Descent Approaches (CDA) have shown to result in considerable reductions of aircraft noise during the approach phase of the flight (Erkelens, 2002). Due to uncertainties in aircraft behaviour, Air Traffic Control (ATC) tends to increase the minimum spacing interval in these approaches, leading to considerable reductions of runway capacity (Clarke, 2000). To enable the application of such procedures in higher traffic volumes, research has advanced in the creation of airborne tools and 4-dimensional prediction algorithms. Little research has addressed the problem of sequencing and merging aircraft in such an ap- proach, however. In this chapter we present the Time-Space Diagram (TSD) display that shows the aircraft along-track distance to the runway versus the time. On this display, the in-trail separation is presented as the horizontal distance between two predictions. It is hy- pothesised that this display will enable the air traffic controller to meter, sequence and merge aircraft flying a CDA at higher traffic volumes. In this chapter, the TSD will be introduced and the effects of various common separation techniques on the predictions of the display are discussed in detail. The display is currently being evaluated by actual air traffic controllers in a simulated traffic scenario to provide an initial validation of the design. Problem statement ATC in CDA procedures According to Annex 11 to the Convention on Civil Aviation (ICAO, 2003), the primary goal of ATC is to provide service for the purpose of safe, orderly and expeditious flow of traffic. In approach control, this task can be described as minimising delays while maintaining suf- ficient separation between the aircraft. During the TDDA, the in-trail distance between two approaching aircraft should therefore reach, but not go below, the minimal distance required. To achieve this, the primary tool common to all approach controllers is the two-dimensional Plan View Display (PVD). This screen shows the, mostly radar-derived, planar positions of the aircraft combined with numeric data on their velocity and altitude. Using this data, the Air Traffic Controller (ATCo) builds a mental model of the traffic scenario, commonly referred to as the “picture” (Nunes & Mogford, 2003). By mentally predicting the trajectories of the aircraft on the screen, the controllers can anticipate on the future spacing and select the ap- propriate actions to adjust spacing if necessary. The certainty of predicting the aircraft future positions depends on the skill of the controller, the behaviour of the aircraft involved and the length of the interval over which the prediction is made (Reynolds et al., 2005). Controller prediction accuracy…
Introduction to Investigating requirements for the design of a 3D weather visualization environment for air traffic controllers
This chapter involves a long-term investigation into the applicability of three-dimensional (3D) interfaces for Air Traffic Control Officers (ATCOs). This investigation is part of collaboration between EUROCONTROL Experimental Centre (EEC) and the Norrköping Visualization and Interaction Studio (NVIS) of Linköping University in which a test-bed was developed in order to evaluate the different features of a 3D interface for ATCOs. This test- bed, known as the 3D-Air Traffic Control (3D-ATC) application, provides controllers with a detailed semi-immersive stereoscopic 3D representation of air traffic. Different aspects of the 3D-ATC application include 3D visualization and interactive resolution of potential conflict between flights (Lange et al., 2006), a voice command interface for visualizing air traffic (Lange et al., 2003), and interactive 3D weather images (Bourgois et al., 2005). Among these various features, the 3D weather visualization was chosen as a first case for carrying out a more accurate users’ study. Weather is considered as one of the major factors contributing to aviation accidents (Spirkovska and Lodha, 2002). As stated by Kauffmann and Pothanun (2000) “weather related accidents comprise 33% of commercial carrier accidents and 27% of General Aviation (GA) accidents”. Moreover, adequate weather information (both for now-cast and forecast information) is often not available to pilots or controllers. The limitation in the way the weather information is represented in current weather displays has been also pointed out in…
Introduction to Time-based Spaced Continuous Descent
Approaches in busy Terminal Manoeuvring Areas Mitigation of aircraft noise for approaching aircraft is an area where considerable improve- ments are still possible through the introduction of noise abatement procedures, such as the Continuous Descent Approach (CDA) (Erkelens, 2000). One of the main issues when imple- menting CDAs is their negative effect on runway throughput, especially during busy oper- ations in daytime. A reduction in landing time intervals might be achieved through precise inter arrival spacing. The combination of aircraft performing the CDA controlled by precise spacing algorithms is seen as one of the solutions to safely increase runway throughput, re- duce delay times for arriving aircraft, and reducing fuel burn, emissions and noise impact (De Gaay Fortman et al., 2007; De Leege et al., 2009; De Prins et al., 2007). The main algorithms used in these researches are all based on the Flap/Gear Scheduler (FGS) developed by Koeslag (2001) and improved by In ‘t Veld et al. (2009). The FGS is evaluated in these researches to investigate the effects of different flight path angles, different types of aircraft, different aircraft weight configurations and different wind conditions on FGS perfor- mance. The FGS is also combined with time and distance based spacing algorithms to ensure proper spacing between aircraft in arrival streams. There are more spacing algorithms developed to control the Time-based Spaced CDA (TSCDA), such as the Thrust Controller (TC) by De Muynck et al. (2008) and the Speed Constraint De- viation controller (SCD), both developed at the National Aerospace Laboratory (NLR). The performances of these three controllers are evaluated in this chapter. Fast-time Monte Carlo Simulations (MCS) are performed using a realistic simulation environment and a realistic sce- nario. The effects of different wind conditions, aircraft weight configuration, arrival stream setup and the position of the aircraft in the arrival stream on the performance of the controllers are also evaluated. In Section 2 the definition of the TSCDA is elaborated by discussing the goals of this concept and by giving the description of the approach used in this research. In Section 3 the working principles of the controllers are discussed. The results of the initial simulations performed to…
Introduction to The potential of some of the innovative operational procedures for increasing the airport landing capacity
Despite continuous efforts by the air transport system operators, regulators, and researchers (academic and consultants), the problem of providing sufficient airport runway capacity to match continuously growing demand safely, efficiently, and effectively has had rather limited success. A[art from growing demand, the specific environmental (mainly noise) constraints at many large airports both in US and Europe have prevented the full utilization of the designed runway capacity. The sharp concentration of atms (air transport movements) (one atm corresponds to one landing or one take off) within the rather short time periods at the hub airports due to operating the hub-and-spoke networks has created sharp peaks causing further already existing imbalance between demand and the available runway capacity. At some other airports one of which is, for example New York La Guardia airport (US), a high demand/capacity imbalance has been created simply because of their attractiveness and not primarily due the type of airline scheduling practice. In addition, specifically in the US, the operation of airports under IMC (Instrument Meteorological Conditions) and VMC (Visual Meteorological Conditions) and the corresponding difference in the ATC (Air Traffic Control) minimum landing distance-based separation rules (IFR – Instrument Flight Rules, and VFR – Visual Flight Rules, respectively) have inherently created instability of the airports’ declared runway landing capacities and consequently their rather high vulnerability to weather conditions. In Europe, such capacity instability caused by weather has also been relatively high, even though the aircraft landings have been carried out exclusively by applying IFR under both IMC and VMC. As well, the shortage of land for expanding the airport runway capacity at many airports has also contributed to the above-mentioned demand/capacity imbalance there…
