Disturbance observer based control with anti-windup applied to a small fixed wing UAV for disturbance rejection

Small Unmanned Aerial Vehicles (UAVs) are attracting increasing interest due to their favourable features; small size, low weight and cost. These features also present different challenges in control design and aircraft operation. An accurate mathematical model is unlikely to be available meaning optimal control methods become difficult to apply. Furthermore, their reduced weight and inertia mean they are significantly more vulnerable to environmental disturbances such as wind gusts. Larger disturbances require more control actuation, meaning small UAVs are far more susceptible to actuator saturation. Failure to account for this can lead to controller windup and subsequent performance degradation. In this work, numerical simulations are conducted comparing a baseline Linear Quadratic Regulator (LQR) controller to integral augmentation and Disturbance Observer Based Control (DOBC). An anti-windup scheme is added to the DOBC to attenuate windup effects due to actuator saturation. A range of external disturbances are applied to demonstrate performance. The simulations conduct manoeuvres which would occur during landing, statistically the most dangerous flight phase, where fast disturbance rejection is critical. Validation simulations are then conducted using commercial X-Plane simulation software. This demonstrates that DOBC with anti-windup provides faster disturbance rejection of both modelling errors and external disturbances

Design and application of advanced disturbance rejection control for small fixed-wing UAVs

Small Unmanned Aerial Vehicles (UAVs) have seen continual growth in both research and commercial applications. Attractive features such as their small size, light weight and low cost are a strong driver of this growth. However, these factors also bring about some drawbacks. The light weight and small size means that small UAVs are far more susceptible to performance degradation from factors such as wind gusts. Due to the generally low cost, available sensors are somewhat limited in both quality and available measurements. For example, it is very unlikely that angle of attack is sensed by a small UAV. These aircraft are usually constructed by the end user, so a tangible amount of variation will exist between different aircraft of the same type. Depending on application, additional variation between flights from factors such as battery placement or additional sensors may exist. This makes the application of optimal model based control methods difficult. Research literature on the topic of small UAV control is very rich in regard to high level control, such as path planning in wind. A common assumption in such literature is the existence of a low level control method which is able to track demanded aircraft attitudes to complete a task. Design of such controllers in the presence of significant wind or modelling errors (factors collectively addressed as lumped disturbances herein) is rarely considered. Disturbance Observer Based Control (DOBC) is a means of improving the robustness of a baseline feedback control scheme in the presence of lumped disturbances. The method allows for the rejection of the influence of unmeasurable disturbances much more quickly than traditional integral control, while also enabling recovery of nominal feedback con- trol performance. The separation principle of DOBC allows for the design of a nominal feedback controller, which does not need to be robust against disturbances. A DOBC augmentation can then be applied to ensure this nominal performance is maintained even in the presence of disturbances. This method offers highly attractive properties for control design, and has seen a large rise in popularity in recent years. Current literature on this subject is very often conducted purely in simulation. Ad- ditionally, very advanced versions of DOBC control are now being researched. To make the method attractive to small UAV operators, it would be beneficial if a simple DOBC design could be used to realise the benefits of this method, as it would be more accessible and applicable by many. This thesis investigates the application of a linear state space disturbance observer to low level flight control of a small UAV, along with developments of the method needed to achieve good performance in flight testing. Had this work been conducted purely in simulation, it is likely many of the difficulties encountered would not have been addressed or discovered. This thesis presents four main contributions. An anti-windup method has been devel- oped which is able to alleviate the effect of control saturation on the disturbance observer dynamics. An observer is designed which explicitly considers actuator dynamics. This development was shown to enable faster observer estimation dynamics, yielding better disturbance rejection performance. During initial flight testing, a significant aeroelastic oscillation mode was discovered. This issue was studied in detail theoretically, with a pro- posed solution developed and applied. The solution was able to fully alleviate the effect in flight. Finally, design and development of an over-actuated DOBC method is presented. A method for design of DOBC for over actuated systems was developed and studied. The majority of results in this thesis are demonstrated with flight test data

Actuator dynamics augmented DOBC for a small fixed wing UAV

This paper presents an actuator dynamics augmentation of the classical Disturbance Observer Based Control (DOBC) approach for control of a small fixed wing Unmanned Aerial Vehcile (UAV). The proposed method modifies the observer to include actuator dynamics. This augmentation allows for significantly higher observer gains in practice than traditional DOBC in the presence of actuator dynamics, resulting in better disturbance rejection performance. The actuator states are unmeasurable, so are also estimated by the proposed observer using an actuator model and the aircraft state. The actuator modelling process of the UAV is provided in detail. The closed-loop stability as well as observer tuning guidelines are discussed. The performance improvement is demonstrated first in numerical simulation and validated with flight test results using a small UAV

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Department of Mechanical EngineeringSmall fixed-wing UAVs are increasingly attracting attention due to many applications in both military operations such as surveillance, and civilian domains such as powerline patrol and aerial photography. Their popularity growth has reduced the weight and size of small fixed-wing UAVs. They are vulnerable to external disturbances, such as wind due to their reduced size and weight. Disturbance has adverse effects on small fixed-wing UAVs, as it lowers the stability and performance of the control system during the operation. The disturbance includes modeling errors caused by the uncertainty of the system parameters as well as the wind from the external environment. The disturbance effects acting on small fixed-wing UAVs must be considered explicitly and eliminated eventually. In this regard, various control techniques for compensating the disturbance have been actively studied in control fields. Representative controller design techniques for compensating the disturbance include robust control (RC), sliding mode control (SMC) and adaptive control (AC), and disturbance observer-based control (DOBC). Most of the control techniques cause a slow response in attenuating disturbance effects since feedback control is performed based on tracking errors. Therefore, there is a need to compensate directly for disturbance through feedforward control. The disturbance observer-based control scheme directly estimates the uncertainty of the system, external disturbance, and directly compensates the estimated disturbance through the feedforward. This paper proposes a nonlinear disturbance observer-based path following controller for a small fixed-wing UAV affected by disturbance such as wind. We used a nonlinear disturbance observer-based control (NDOBC) method for precise path following for a small fixed-wing UAV along with the Lyapunov guidance vector field (LGVF) technique. The disturbance is estimated by a nonlinear disturbance observer, and then it is incorporated into the LGVF path following controller to compensate disturbance effects. The numerical simulation is first carried out through the MATLAB Simulink environment. Software in the loop simulation (SITL) is carried out to verify its performance. Outdoor flight experiments are performed to demonstrate its performance in a real-world environment.ope

Aircraft Modeling and Simulation

Various aerodynamics, structural dynamics, and control design and experimental studies are presented with the aim of advancing green and morphing aircraft research. The results obtained with an in-house CFD code are compared and validated with those of two NASA codes. The aerodynamical model of the UAS-S45 morphing wing as well as the structural model of a morphing winglet are presented. A new design methodology for oleo-pneumatic landing gear drop impact dynamics is presented as well as its experimental validation. The design of a nonlinear dynamic inversion (NDI)-based disturbance rejection control on a tailless aircraft is presented, including its validation using wind tunnel tests

Application of robust control in unmanned vehicle flight control system design

The robust loop-shaping control methodology is applied in the flight control system design of the Cranfield A3 Observer unmanned, unstable, catapult launched air vehicle. Detailed linear models for the full operational flight envelope of the air vehicle are developed. The nominal and worst-case models are determined using the v-gap metric. The effect of neglecting subsystems such as actuators and/or computation delays on modelling uncertainty is determined using the v-gap metric and shown to be significant. Detailed designs for the longitudinal, lateral, and the combined full dynamics TDF controllers were carried out. The Hanus command signal conditioning technique is also implemented to overcome actuator saturation and windup. The robust control system is then successfully evaluated in the high fidelity 6DOF non-linear simulation to assess its capability of launch stabilization in extreme cross-wind conditions, control effectiveness in climb, and navigation precision through the prescribed 3D flight path in level cruise. Robust performance and stability of the single-point non-scheduled control law is also demonstrated throughout the full operational flight envelope the air vehicle is capable of and for all flight phases and beyond, to severe launch conditions, such as 33knots crosswind and exaggerated CG shifts. The robust TDF control law is finally compared with the classical PMC law where the actual number of variables to be manipulated manually in the design process are shown to be much less, due to the scheduling process elimination, although the size of the final controller was much higher. The robust control law performance superiority is demonstrated in the non-linear simulation for the full flight envelope and in extreme flight conditions

Autonomous Landing and Go-around of Large Jets Under Severe Weather Conditions Using Artificial Neural Networks

We introduce the Intelligent Autopilot System (IAS) which is capable of autonomous landing, and go-around of large jets such as airliners under severe weather conditions. The IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to autonomously handle flight uncertainties such as severe weather conditions, autonomous complete flights, and go-around. A robust approach to control the aircraft's bearing using Artificial Neural Networks is proposed. An Artificial Neural Network predicts the appropriate bearing to be followed given the drift from the path line to be intercepted. In addition, the capabilities of the Flight Manager of the IAS are extended to detect unsafe landing attempts, and generate a go-around flight course. Experiments show that the IAS can handle such flight skills and tasks effectively, and can even land aircraft under severe weather conditions that are beyond the maximum demonstrated landing of the aircraft model used in this work as reported by the manufacturer's operations limitations. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots