Automatic Flight Control Systems

The history of flight control is inseparably linked to the history of aviation itself. Since the early days, the concept of automatic flight control systems has evolved from mechanical control systems to highly advanced automatic fly-by-wire flight control systems which can be found nowadays in military jets and civil airliners. Even today, many research efforts are made for the further development of these flight control systems in various aspects. Recent new developments in this field focus on a wealth of different aspects. This book focuses on a selection of key research areas, such as inertial navigation, control of unmanned aircraft and helicopters, trajectory control of an unmanned space re-entry vehicle, aeroservoelastic control, adaptive flight control, and fault tolerant flight control. This book consists of two major sections. The first section focuses on a literature review and some recent theoretical developments in flight control systems. The second section discusses some concepts of adaptive and fault-tolerant flight control systems. Each technique discussed in this book is illustrated by a relevant example

Fault tolerant control for nonlinear aircraft based on feedback linearization

The thesis concerns the fault tolerant flight control (FTFC) problem for nonlinear aircraft by making use of analytical redundancy. Considering initially fault-free flight, the feedback linearization theory plays an important role to provide a baseline control approach for de-coupling and stabilizing a non-linear statically unstable aircraft system. Then several reconfigurable control strategies are studied to provide further robust control performance:- A neural network (NN)-based adaption mechanism is used to develop reconfigurable FTFC performance through the combination of a concurrent updated learninglaw. - The combined feedback linearization and NN adaptor FTFC system is further improved through the use of a sliding mode control (SMC) strategy to enhance the convergence of the NN learning adaptor. - An approach to simultaneous estimation of both state and fault signals is incorporated within an active FTFC system.The faults acting independently on the three primary actuators of the nonlinear aircraft are compensated in the control system.The theoretical ideas developed in the thesis have been applied to the nonlinear Machan Unmanned Aerial Vehicle (UAV) system. The simulation results obtained from a tracking control system demonstrate the improved fault tolerant performance for all the presented control schemes, validated under various faults and disturbance scenarios.A Boeing 747 nonlinear benchmark model, developed within the framework of the GARTEUR FM-AG 16 project “fault tolerant flight control systems”,is used for the purpose of further simulation study and testing of the FTFC scheme developed by making the combined use of concurrent learning NN and SMC theory. The simulation results under the given fault scenario show a promising reconfiguration performance

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Development of Fault Detection and Diagnosis Techniques with Applications to Fixed-wing and Rotary-wing UAVs

ABSTRACT Development of Fault Detection and Diagnosis Techniques with Applications to Fixed-wing and Rotary-wing UAVs Ling Ma Fault Detection and Diagnosis (FDD), as the central part of a Fault Tolerant Control System (FTCS), detects and diagnoses the source and the magnitude of a fault when a fault/failure occurs either in an actuator, sensor or in the system itself. This thesis work develops an applicable procedure for a FDD scheme to both fixed-wing and rotary-wing UAVs (Unmanned Aerial Vehicles) in the discrete-time stochastic domain based on the Kalman filter techniques. In particular, the proposed techniques are developed in highly nonlinear and 6 degree-of-freedom equations of Matlab/Simulink simulation environment for a quad-rotor helicopter UAV, a Boeing 747, and a NASA Generic Transport Model (GTM) fixed-wing UAV. A key development in this thesis is that an Adaptive Two-Stage Extended Kalman Filter (ATSEKF) algorithm and a Dual Unscented Kalman Filter (DUKF) algorithm are applied for simultaneous states and fault parameters estimation of these UAVs. The statistical decision-making techniques for fault detection and diagnosis are also discussed in the presence of partial faults in the UAVs. The measured system outputs and control signals are used as inputs of the ATSEKF and DUKF, and the estimated states and parameters are used for comparison and analysis in the fault detection and diagnosis. The simulation results show that the effectiveness and performance of ATSEKF and DUKF for the purpose of fault detection and diagnosis of both fixed- and rotary-wing UAVs are satisfactory

Model based fault detection and isolation approach for actuator and sensor faults in a UAV

Thesis (MEng)--Stellenbosch University, 2021.ENGLISH ABSTRACT: This thesis presents the design and validation of model-based fault detection and isolation (FDI) approach for unmanned aerial vehicles (UAV). In safety-critical sys- tems such as chemical, nuclear plants and passenger aircraft, FDI is typically founded on hardware redundancy. In hardware redundancy, multiple actuators are spatially distributed to localise faults quickly, and sensor measurements are compared for consistency. The primary drawback with hardware redundancy is the increased installation complexity, weight, and costs. With modern computing technologies, model-based FDI offers a cost-effective, iterative and efficient FDI design process, verifiable with high fidelity computer-aided simulation (CAS). This thesis investigates the application of the Two-Stage Kalman filter (TSKF) to the problem of FDI. The TSKF solves the main deficiencies faced with the aug- mented state Kalman filter (ASKF), namely, numerical instability in ill-conditioned systems, and computational inefficiency where large parameter vectors are aug- mented. The TSKF approach utilises two parallel reduced-order KFs to estimate the system state and the parameter vectors separately. The UAV’s two rudders are not "isolable" because they produce identical moments. A novel active FDI (AFDI) method is proposed to isolate rudder actuator faults. The FDI displays high noise sensitivity under the evere Dryden turbulence model, resulting in high false detection and missed detection rates. A novel adap- tive technique is proposed to improve the robustness and sensitivity of the FDI. Unlike most methods which rely on a single scaling factor, the proposed adaptation technique employs multiple factors to weight the spread of fault parameter covari- ance matrix in the direction of flow of information, resulting in selective adaptation. Fault parameter variations are nonuniform in time and space. A static alarm threshold will induce high false alarms or missed alarms when set to low or too high, respectively. A novel adaptive threshold based on the normalised innovation squared (NIS) is proposed. A Monte Carlo campaign is carried out to validate the FDI while fault-sizes, the aircraft’s physical parameters, and disturbances are scat- tered, each with a distinct mean dispersion. The proposed strategy exhibits high robustness to noise and sensitivity to faults which indicates a reliable FDI.AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die ontwerp en validering van ‘n model-gebaseerde foutop- sporing en isolasie (“fault deteciton and isolation (FDI)”) tegniek vir onbemande lugvoertuie (“unmanned aerial vehicles (UAVs)”). In veiligheidskritieke stelsels soos chemiese aanlegte, kernkragaanlegte, en passasiersvliegtuie, word FDI gewoon- lik gebaseer op hardeware-oortolligheid. Vir hardeware-oortolligheid word verskeie aktueerders ruimtelik versprei om foute vinnig op te spoor, en sensormetings word vergelyk vir ooreenstemming. Die primêre nadeel van hardeware-oortolligheid is die verhoogde installasie-kompleksiteit, gewig en koste. Met moderne rekenaarteg- nologieë bied model-gebaseerde FDI ’n koste-effektiewe, iteratiewe en doeltref-fende FDI-ontwerpproses met ‘n hoë betroubaarheid wat bevestig kan word met rekenaargesteunde simulasie. Hierdie tesis ondersoek die toepassing van die twee-stadium Kalman filter (“two- stage Kalman filter (TSKF)”) op die probleem van FDI. Die TSKF los die belangrik- ste tekortkominge van die uitgebredie-toestand Kalman-filter (“augmented state Kalman filter (ASKF)”) op, naamlik numeriese onstabiliteit in swak gekondisioneerde stelsels, en berekeningsondoeltreffendheid waar groot parametervektore bygevoeg word. Die TSKF-benadering gebruik twee parallelle Kalman filters met vermin- derde orde om die stelseltoestand en die parametervektore afsonderlik af te skat. Die UAV se twee roere (“rudders”) is egter nie “isoleerbaar” nie omdat dit hulle identiese draaimoment veroorsaak. ’n Nuwe aktiewe FDI-metode (AFDI) word voorgestel om die roeraktueerderfoute te isoleer. Die FDI vertoon hoë sensitiwiteit vir geraas vanaf erge turbulensie soos gemod- elleer deur die Dryden-turbulensie-model, wat lei tot ‘n groot aantal vals deteksies en gemiste deteksies. ’n Nuwe aanpassingstegniek word voorgestel om die robu- ustheid en sensitiwiteit van die FDI te verbeter. Anders as die meeste metodes wat op een enkele skaalfaktor staatmaak, gebruik die voorgestelde aanpassingstegniek verskeie faktore om die verspreiding van die foutparameterkovariansiematriks in die rigting van informasievloei te weeg, wat lei tot selektiewe aanpassing. Foutparametervariasies is nie eenvormig in tyd of ruimte nie. ’n Statiese alar- mdrempel sal hoë vals deteksies of gemiste deteksies veroorsaak as dit onderskei-delik óf te laag óf te hoog gestel is. ’n Nuwe aanpassingsdrempel wat gebaseer is op die genormaliseerde innovasie kwadraat (NIS) word voorgestel. ’n Monte Carlo simulasieveldtog is uitgevoer om die FDI te toets met die foutgroottes, die fisiese parameters van die vliegtuig, en die steurings lukraak gevarieer elk met ’n duide- like gemiddelde verspreiding. Die voorgestelde strategie vertoon ’n hoë robuus- theid vir geraas en sensitiwiteit vir foute, wat dui op ’n betroubare FDI

Fault-Tolerant Control with Applications to Aircraft Using Linear Quadratic Design Framework

Safety is one of the major concerns in the aviation community for both manned aircraft and unmanned aerial vehicles (UAVs). The safety issue of manned aircraft, such as commercial aircraft, has drawn great attentions especially after a series of disasters in recent decades. Safety and reliability issues of UAVs have also attracted significant attention due to their highly autonomous feature towards their future civilian applications. Focusing on the improvement of safety and reliability of aircraft, a fault-tolerant control (FTC) system is demanded to utilize the configured redundancy in an effective and efficient manner to increase the survivability of aircraft in the presence of faults/failures. This thesis aims to develop an effective FTC system to improve the security, reliability, and survivability of the faulty aircraft: manned aircraft and UAVs. In particular, the emphases are focused on improving the on-line fault-tolerant capability and the transient performance between faults occurrence and control re-configuration. In the existing fault-tolerant literature, several control approaches are developed to possess fault-tolerant capability in recent decades, such as sliding mode control (SMC), model reference adaptive control (MRAC), and model predictive control (MPC), just as examples. Different strategies have their specific benefits and drawbacks in addressing different aspects of fault-tolerant problems. However, there are still open problems in the fault-tolerant performance improvement, the transient behavior management, consideration of the interaction between FTC and fault detection and diagnosis (FDD), etc. For instance, MPC is recognized as a suitable inherent structure in synthesizing a FTC system due to its capability of addressing faults via solving constraints, reforming cost function, and updating model on-line. However, this on-line FTC capability introduces further challenges in terms of fault problem formulation, on-line computation, transient behavior before reconfiguration is triggered, etc. Designing an efficient FDD is also a challenge topic with respect to time response speed, accuracy, and reliability due to its interaction with a fault-tolerant controller. In the control design framework based on linear quadratic (LQ) cost function formulation, faults can be accommodated in both passive and active way. A passive FTC system is synthesized with a prescribed degree of stability LQ design technique. The state of the post-fault system is obtained through state-augmented extended Kalman filter (SAEKF), which is a combined technique with state and parameter estimation. In terms of reconfiguration capability, MPC is considered as a favorable active FTC strategy. In addition to MPC framework, the improvement of on-line computational efficiency motivates MPC to be used to perform fault-tolerant flight control. Furthermore, a Laguerre-function based MPC (LF-MPC) is presented to enhance the on-line fault-tolerant capability. The modification is based on a series of Laguerre functions to model the control trajectory with fewer parameters. In consequence, the computation load is reduced, which improves the real-time fault-tolerant capability in the framework of MPC. The FTC capability is further improved for accommodating the performance degradation during the transient period before the control reconfiguration. This approach is inspired by exponentially increasing weighting matrix used in linear quadratic regulator (LQR). Two platforms are used to perform the evaluation of the designed FTC system. A quadrotor UAV, named the Qball-X4, is utilized to test FTC designed with exponentially increasing weighing matrix LQ technique and FDD designed with SAEKF. The evaluation is conducted under the task of trajectory tracking in the presence of loss of control effectiveness (LOE) faults of actuators. The modified MPC is utilized to synthesize an active FTC system to accommodate the elevator stuck fault of a Boeing 747-100/200 benchmark model. The exponentially increasing weighing matrix LQ technique is further implemented in LF-MPC framework to improve the fault-tolerant capability before the control reconfiguration. A time delayed FDD is integrated into the evaluation process to present the effectiveness of the proposed FTC strategies. The designed FTC system is evaluated under the emergency landing task in the event of failure of elevators

Fault tolerant flight control system design for unmanned aerial vehicles

Safety and reliability of air vehicles is of the utmost importance. This is particularly true for large civil transport aircraft where a large number of human lives depend on safety critical design. With the increase in the use of unmanned aerial vehicles (UAVs) in our airspace it is essential that UAV safety is also given attention to prevent devastating failures which could ultimately lead to loss of human lives. While civil aircraft have human operators, the pilot, to counteract any unforeseen faults, autonomous UAVs are only as good as the on board flight computer. Large civil aircraft also have the luxury of weight hence redundant actuators (control surfaces) can be installed and in the event of a faulty set of actuators the redundant actuators can be brought into action to negate the effects of any faults. Again weight is a luxury that UAVs do not have. The main objective of this research is to study the design of a fault tolerant flight controller that can exploit the mathematical redundancies in the flight dynamic equations as opposed to adding hardware redundancies that would result in significant weight increase. This thesis presents new research into fault tolerant control for flight vehicles. Upon examining the flight dynamic equations it can be seen, for example, that an aileron, which is primarily used to perform a roll manoeuvre, can be used to execute a limited pitch moment. Hence a control method is required that moves away from the traditional fixed structure model where control surface roles are clearly defined. For this reason, in this thesis, I have chosen to study the application of model predictive control (MPC) to fault tolerant control systems. MPC is a model based method where a model of the plant forms an integral part of the controller. An optimisation is performed based on model estimations of the plant and the inputs are chosen via an optimisation process. One of the main contributions of this thesis is the development of a nonlinear model predictive controller for fault tolerant flight control. An aircraft is a highly nonlinear system hence if a nonlinear model can be integrated into the control process the cross-coupling effects of the control surface contributions can be easily exploited. An active fault tolerant control system comprises not only of the fault tolerant controller but also a fault detection and isolation subsystem. A common fault detection method is based on parameter estimation using filtering techniques. The solution proposed in this thesis uses an unscented Kalman filter (UKF) for parameter estimation and controller updates. In summary the main contribution of this thesis is the development of a new active fault tolerant flight control system. This new innovative controller exploits the idea of analytical redundancy as opposed to hardware redundancy. It comprises of a nonlinear model predictive based controller using pseudospectral discretisation to solve the nonlinear optimal control problem. Furthermore a UKF is incorporated into the design of the active fault tolerant flight control system

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones