Top PDF Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles

Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles

Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles

The simulation scenario requires the UAV to reduce the airspeed from 35m/s to 25m/s while maintaining a constant height at 100m. Since the airspeed spans a large range across the flight envelope, the nonlinearity of the dynamics will be excited. The tracking results of this case study are given in Fig.7 and 8, which manifest that the controller design based on the NDI technique is able to deal with the nonlinear dynamics and provide stability. However, the baseline NDI controller in this scenario demonstrates notable tracking errors especially on the height channel due to the combination of the external disturbance and internal uncertainties. The NDI controller with integral action gives a better tracking accuracy than the baseline controller. Nevertheless, compared to DOBC it yields a more oscillatory outputs and its correction is much slower as it needs the error signals to accumulate adequately to take actions. This becomes more obvious at 60s of the simulation when the vertical wind speed starts to change from −2m/s to 2m/s. The corresponding height output under the integral NDI control results in a large deviation from the reference signal, whereas the output under DOBC quickly recovers to the reference signal because it exploits the estimated disturbances in a feed-forward fashion.
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Fault Tolerant Flight Control of Unmanned Aerial

Vehicles

Fault Tolerant Flight Control of Unmanned Aerial Vehicles

Several research works have been conducted toward the fixed-wing UAV mainly fo- cused on the control and navigation. In [47], a PID controller is used to control the airspeed and altitude of a fixed-wing UAV. The control structure provides a low level control nec- essarily for mission planning and execution. In [48] a hierarchical approach for real-time motion planning is used for a small fixed-wing UAV. The proposed approach divides the tra- jectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. In [49], an optimization-based tuning for PID controllers is presented in order to minimize the requirement of in-flight “tuning” and substantially reduce the risks and costs involved in flight tests. Also, a design of a robust PID controller scheme that combines the deadbeat response, robust control, and model reduction techniques is described in [50] to enhance the performance and robustness of PID controller. The design scheme is applied for the pitch control of a fixed-wing UAV. This chapter focuses on the application of fault-tolerant control to a fixed-wing UAV based on widely used PID control technique. The PID controller is used to control all six variables separately, which are Roll, Pitch, Yaw (Euler angles), X, Y and Z (Navigation) for both fault-free and faulty scenarios. For fault cases, the Gain-Scheduled PID (GS-PID) control strategy is used to handle the hard-over failure injected to UAV with split rudders. Different sets of pre-tuned PID gains are designed for different parts of flight envelope and fault cases. The results are presented at the end of the chapter.
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Visual Sense-and-Avoid for Small Size Unmanned Aerial Vehicles

Visual Sense-and-Avoid for Small Size Unmanned Aerial Vehicles

In [52] a biomimetic visual sensing and control system for MAVs are introduced. In the paper two models are introduced for visual navigation in insects: an optic flow based approach, when the insect uses its compound eye for depth and range sensing and collision avoidance, and another visual sensing based on the ocelli organ for flight stabilization. In the paper it is shown how insects use these sensing information in different tasks, for example for landing or hovering. The available OF sensor chips and artificial ocelli sensor is introduced with the control algorithms. At the time the system was capable of flying at low altitudes (some meters) and following a shallow (±10°) terrain. The development of the system is still in progress. This system is designed for micro air vehicles and for flapping-wing, insect-like robots, which typically fly at low altitudes. The main advantage of the system is that it will be cheap and extremely lightweight. The main drawback is that because of the OF algorithm it cannot be scaled up for a bigger UAS and that it needs special hardware elements (OF chip).
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Disturbance observer based control with anti-windup applied to a small fixed wing UAV for disturbance rejection

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

For small UAVs, however, wind gusts present a considerably different challenge. The significant reduction in size leads to lower inertia, making small UAVs more sensitive to disturbance [5]. Moreover, a reduction in aircraft size is generally accompa- nied by a reduction in operating airspeed. This has reached a critical point for small UAVs where their operating airspeeds are of the same magnitude as the gust disturbances they are subjected to [6]. As a consequence, in gust alleviation for small UAVs, structural loads are less critical condition than flight performance. This presents a different problem of disturbance rejection, with small UAVs having very different considerations. Flight control can be gener- alised into two categories; outer loop trajectory control and inner loop attitude control. UAV trajectory track- ing in wind has been studied in literature, with a range of methods applied. Vector field guidance has demonstrated robustness to wind disturbance by util- ising ground speed and course for navigation [7]. It was shown that path planning with a known constant wind can improve mission accuracy and efficiency [8]. By using pre-computed information of aircraft turn- ing performance in wind, it has also been shown that path following in wind can be improved [9]. These methods demonstrate that using robust methods is feasible for trajectory tracking in wind. Using infor- mation of the wind improves performance further. The limitation being that accurate prior wind knowledge is not feasible, especially for gust disturbances. As small UAVs are highly affected by gusts, it should be considered in their operation. It has been shown that online estimation of steady wind can be obtained and used in trajectory following, with some ability to track variance [10]. Disturbance Observer Based Control (DOBC) augmentation has shown good per- formance in simulation and flight testing in rejecting disturbance of an unknown wind in trajectory track- ing [11]. Inner loop control for UAVs is also widely studied in literature. However, work regarding distur- bance rejection in this area is more sparse, particularly regarding external disturbances. Disturbance rejection for parameter uncertainty has been addressed by var- ious approaches including robust methods [12], Neu- ral Networks (NNs) [13], Support Vector Regression (SVR) [14] and Active Disturbance Rejection Control (ADRC) [15]. While these works were able to account
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Integrated flight/thrust vectoring control for jet-powered unmanned aerial vehicles with ACHEON propulsion

Integrated flight/thrust vectoring control for jet-powered unmanned aerial vehicles with ACHEON propulsion

However, little research has been devoted to the study of TVC to novel jet UAVs. Vinayagam and Sinha [20] have proposed a TVC control strategy for mechanical canted nozzle based jet aircraft-F-18/HARV and have assessed its Velocity Vector Roll (VVR) maneuverability. However, the nozzle has limited deflection angle and it is only suitable for pilot-operated aircraft applications. Yang [21] and Yuhua [22] [15] studied the integrated flight controls for fixed-wing UAV with TV and individually designed the PID control strategy for longitude and latitude control, but it is difficult to decouple the 6-DOF controller channels because of nonlinearities in the fixed-wing aircraft aerodynamics and the limitations of the PID linear control law. It is also especially difficult to implement super-maneuverability flight actions based solely on PID control methods. Bajodah and Hameduddin [16] , Wang and Stengel [23] and Lodge and Fielding [24] have conducted research on TVC for fixed-wing UAV’s based on Nonlinear Dynamics Inversion (NDI), but it only included an inner control loop for attitude angular rate control and an outer loop for attitude angle control in the wind-frame; it is therefore not applicable for completely self-positioning control of fixed wing UAV’s utilizing TV.
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Path Planning For Persistent Surveillance Applications Using Fixed-Wing Unmanned Aerial Vehicles

Path Planning For Persistent Surveillance Applications Using Fixed-Wing Unmanned Aerial Vehicles

product design, where the addition of points can be quite complex where aesthetics are in- volved. However, in the case of path planning, planners can be designed so that points are densely packed or, at least, uniformly distributed. With this mandate for the set of points defining a path or constraint to be approximated with a parametric curve, we are able to use chordal-parametrization to approximate arc-length parametrization without incurring the bulges, loops, and wiggles often induced when parametrization is not straightforward. Problems could arise if a sparse/minimal point set is used to define an obstacle or con- straint but in this case it is straightforward to add uniformly-spaced points to avoid the aforementioned problems. Depending on the nature of the defining points, a simple chordal parametrization, as is presented in Section 7.2.2, may be used in conjunction with a least squares optimization to fit the data to a curve. If the obstacle shape includes multiple sharp vertices, it is best broken into a series of splines with C 0 continuity at the vertices. If chordal parametrization does not yield adequate results, Speer et al [86] have developed a global re-parametrization method which, they report, leads to dramatically better results. In the event obstacle data are in the form of a point cloud, with inherently noisy data, then a regularization term can be added to the least squares error to ensure a smooth so- lution curve. A survey of these approaches is provided by Wang et al [87]. By assigning at least three control points for each shape defining point (bounds, inflection points, locations of peak curvature, etc.) in a set of defining points and then uniformly distributing these points in the least squares solution permits the approximating curves to be well behaved and closely adhere to the original set. An bound can be placed on the peak error between the defining set of points and the resultant curve and additional control points then used to attain a desired fidelity to the defining set. The heuristic we use performs with errors in precision that are on the order of those caused by atmospheric disturbances to the flight path.
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A Data Driven Adaptive Method for Attitude Control of Fixed Wing Unmanned Aerial Vehicles

A Data Driven Adaptive Method for Attitude Control of Fixed Wing Unmanned Aerial Vehicles

DOI: 10.4236/aast.2019.41001 12 Advances in Aerospace Science and Technology most widely used CPID-based controller that lacks dynamic model obtaining capabilities, its deflection angles at each sampling point are not optimum and thus cannot cover the whole flight envelop with best performance (the control law may be optimal in a certain short time range, see 0 - 2.3 seconds in first and third enlarged figures in Figure 10, but not in the whole range). As for the NDI method, it relies on accurate model information and some of them such as the external disturbances are hard to be obtained, resulting in poor robustness of this method. Besides, the deflection angles by NDI are not necessarily optimum at each sampling point under unmodeled uncertainties.
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Control system for fixed-wing unmanned aerial vehicles: automatic tuning, gain scheduling, and turbulence mitigation

Control system for fixed-wing unmanned aerial vehicles: automatic tuning, gain scheduling, and turbulence mitigation

where some of the parameters are very small. Secondly, the actuator and sensor dy- namics are often ignored. Thirdly, some nonlinear parameters may vary depending on operating points. The combination of measurement errors, neglected dynamics and parameter variations significantly affect the accuracy of the model; hence, the simulation platform is not an ideal representative of the UAV’s behaviour. Thus, the controllers that have been adjusted and tuned in the simulation platform will not always be able to achieve the desired performance when deployed on the ac- tual aircraft. Tuning in-flight can be difficult for inexperienced pilots and this can lead to crashes, which may result in broken expensive electronic parts. These fac- tors have motivated the development of the automatic tuning for fixed-wing UAVs flight control system, that is able to identify suitable PID controller parameters with the minimal use of human interaction.
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Auto-Landing System for Fixed-Wing Unmanned Aerial Vehicle

Auto-Landing System for Fixed-Wing Unmanned Aerial Vehicle

microcontrollers, control systems and flight simulation software are widely available. These many recent developments make it possible to design a fixed- wing platform that can be operated autonomously. This thesis focused on the development of a UAV landing algorithm for use with a simple cost-effective platform that, with some added componentry and modifications, is capable of flying autonomously, locating the runway, and landing safely. The development of affordable and easy to pilot unmanned autonomous vehicles can be enabled by technologies as proven in this thesis. The availability of powerful development and analysis tools such as MATLAB and flight simulators contribute to
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Fault-tolerant flight control for a fixed-wing unmanned aerial vehicle with partial horizontal and vertical stabiliser losses

Fault-tolerant flight control for a fixed-wing unmanned aerial vehicle with partial horizontal and vertical stabiliser losses

This flight test was used to obtain telemetry data for the aircraft under RC flight. This provided information that gave confidence in the sensors and also provided insight into how the different damage configurations affected the dynamics of the aircraft. After the first few flight tests, it was safe to say that the aircraft can be flown in the different damage configurations. The avionics were reinstalled on the aircraft and calibrated accordingly. The full estimator was enabled and the telemetry logged for investigation after the flight. The healthy aircraft was flown in its symmetric configuration to provide a baseline of the behaviour of the aircraft. The take-off and landing were both performed on the healthy configuration. The aircraft was then flown with partial horizontal and partial vertical stabiliser respectively and finally combined. For each of these flights, the aircraft took off, executed manoeuvres and landed in the respective damage configuration. A flight with a transition from healthy to damaged was also conducted to provide insight into the dynamics of the aircraft as its model changed. The transition flight had the safety pilot take off with the healthy aircraft. During flight, the transition was triggered. The safety pilot then landed the aircraft in the damage configuration. Under RC testing, the safety pilot was constantly making corrections to the aircraft to ensure that it stayed in the air. As a result of these constant corrections, small events, such as the transitions or doublets, were not observable from the sensor measurements.
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Cooperative Control for Landing a Fixed-Wing Unmanned Aerial Vehicle on a Ground Vehicle

Cooperative Control for Landing a Fixed-Wing Unmanned Aerial Vehicle on a Ground Vehicle

influence the lift-to-drag ratio and thereby the flight dynamics. The aircraft geometry, together with its weight and balance, will also define the moments acting on the aircraft and thus determine important flight properties related to stability and control. The properties of a perfect airfoil of infinite length can be described in terms of three dimensionless coefficients - lift, drag and pitching moment. When considering a real aircraft the surfaces are of finite length and so the airfoil model is only an approximation. In addition to this, the flow around neighboring surfaces create interference effects, resulting in complex flows which are difficult to describe analytically. This, combined with the fact that the details of how flight is achieved is still not completely understood today, makes flight theory largely dependent on either experimental measurements in wind tunnels, flight testing or numerical methods such as computational fluid dynamics (CFD) or simpler methods like VLM (vortex lattice methods). For small UAVs the latter is typically sufficient for modeling, although testing on real models will in general give more precise results.
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Development Of Robust Flight Control Design For Unmanned Aerial Helicopter

Development Of Robust Flight Control Design For Unmanned Aerial Helicopter

Unmanned Aerial Vehicles (UAVs) have attracted significant interests worldwide during the last decade [1]. Among all kinds of UAVs, the miniature coaxial helicopter is one of the most promising platform due to its augmented stability and compact footprint. It is most suitable to fly in confined spaces such as indoor [2]. However, helicopters are more difficult to be controlled than fixed-wing aircraft. They are cross-coupled systems with dynamics changing drastically under different flight conditions. Miniature coaxial helicopters present some unique characteristics different from other types of helicopters, such as more complex aerodynamics of the dual-rotor system and payload limitation on sensors. All these have made the control task complicated and challenging. A few methods have been explored to design autonomous control system for miniature unmanned helicopters [2], [3], [4], [5], [6], [7], [8], [9]. Some of them were only tested by simulation, while some of them consider only parts of the helicopter system. Hence, low- level autonomous control of miniature coaxial helicopters remains a fundamental problem to be solved. Our research team has upgraded several hobby helicopters as autonomous flight testbeds. One of them is from the ESky Big Lama as shown in Fig. 1. Many research works have been done on this platform, including mechanical modification for additional payload, construction of a low cost avionic system for autonomous flight, and nonlinear dynamics modeling for control design and verification [10]. This paper aims to design a complete autonomous control system to stabilize the helicopter and to make it hover in the corner of two walls or fly along a wall. The outline of this paper is as follows. Section II discusses how to formulate the coaxial helicopter system into a dual loop control structure. Section III presents a thorough design procedure of H∞ control for the inner stabilization loop, followed by Section IV, which presents a thorough design procedure of the robust and perfect tracking control for the outer navigation loop. Section V demonstrates the performance of the closed-loop system by carrying out a hovering flight test in the corner of two walls and a forward flight test along a wall. At last, Section VI concludes all the work.
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Design and application of advanced disturbance rejection control for small fixed-wing UAVs

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

A recent and detailed investigation [19] into the effects of UAV characteristics and their effects on gust response made some interesting conclusions. It was found that a reduced wing loading reduced UAV response to gust in all metrics apart from pitch; a result which is not necessarily expected. A number of other aircraft design considerations were also discussed specifically regarding wind turbulence handling. Although this information is useful in the design of future aircraft, quite often the small UAVs operated use a generic airframe, adapted for purpose. If this problem could be addressed through control design, the solution would be more generic and cheaper to implement. A detailed study by Mohamed [20] concluded that current sensors, such as gyroscopes and accelerometers may be insufficient for counteracting turbulence for Micro Aerial Vehicles (MAVs). However, MAVs are substantially smaller than UAVs and therefore even more susceptible to wind disturbances. It was concluded that combining multiple sensor readings together would provide far better response. This means that a multiple input control system is preferable, as the effect of turbulence may not easily be obtained from a single sensor. Traditional control methods such as PID are limited in this sense.
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Advanced take-off and flight control algorithms for
fixed wing unmanned aerial vehicles

Advanced take-off and flight control algorithms for fixed wing unmanned aerial vehicles

Both the above mentioned problems can be overcome by simplifying the SAM controller so that no acceleration along the trajectory is required, thus using the aircraft’s axial acceleration solely for airspeed regulation. This simplifies the 3D acceleration vector to a 2D commandable acceleration vector which is always perpendicular to the trajectory. This plane that remains perpendicular to the trajectory and passes through the aircraft’s current position, will be cal- led the Trajectory plane. The control then reduces to regulating the aircraft’s position on the Trajectory plane and the aircraft’s airspeed. The Trajectory plane will move along the trajec- tory as the aircraft travels along it. This makes the architecture position dependent and will be called Position-based Kinematic Guidance (PKG).
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Autonomous Vertical Recovery  of Fixed Wing Unmanned Aerial Vehicles

Autonomous Vertical Recovery of Fixed Wing Unmanned Aerial Vehicles

In Figure 27, is the roll angle, is the roll rate, is the roll command applied to the plant and is the commanded roll rate. The plant is represented by the first order transfer function and the integrator. The roll rate is conveniently available as an IMU gyro signal. The rate feedback is multiplied by the derivative gain, and is then summed to the input signal provided by the pilot control. The gain is easily tuned during flight testing by increasing gain until the system starts to oscillate and then slightly backing off the gain until the oscillations stop. Note that the defined first order plant in theory would not exhibit this tendency; however, higher order dynamics come into play in practice. When properly tuned, the system counteracts external disturbances and requires more input from the pilot to generate the desired manoeuvres. Increasing the input gain allows the pilot control input to remain unaffected while still damping the system to any disturbances. Rate mode can be used as a manual control autopilot safety override should the autonomous system react
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Autonomous landing of a fixed-wing unmanned aerial vehicle using differential GPS

Autonomous landing of a fixed-wing unmanned aerial vehicle using differential GPS

In 2008 Peddle submitted his thesis for a Doctorate in Philosophy in engineering, with the title of “Acceleration-based manoeuvre flight control systems for unmanned aerial vehicles” [1]. This work became a cornerstone for many of the projects in the ESL, including this present project. The focus of Visser's master's dissertation [11] on the topic of ATOL is the autonomous precision landing of a UAV by incorporating research from [1] and vision-based sensors. Visser, like Roos, made use of a standard GPS to guide the UAV onto the final approach of the runway. During the landing phase, vision sensors were used to obtain accurate position and altitude data. The camera system relied on beacons that were placed near the landing target to obtain reliable data. The camera system that Visser developed was tested successfully. Unfortunately, a radio frequency (RF) failure on the UAV resulted in the destruction of the vehicle, preventing a successful landing. De Hart [12] used the same aircraft to extend Peddle's research by implementing advanced take-off and control algorithms for fixed-wing UAVs.
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Autonomous landing of a fixed-wing unmanned aerial vehicle onto a moving platform

Autonomous landing of a fixed-wing unmanned aerial vehicle onto a moving platform

The landing accuracy was evaluated using the results from a significant number of simulations. Although the typical Monte Carlo method requires many times more simulations to be truly representative, this was practically infeasible as it takes a large amount of time to set up each simulation, perform it, and extract the data afterwards. This task also required manual intervention and therefore could not be automated. The simulations were therefore limited to ten runs for each scenario considered, assuming that there is an equal probability that the aircraft will be subjected to each scenario in practice. The scenarios included zero wind and wind from every direction for both stationary and moving platform landings, resulting in one hundred total tests which was deemed sufficient to perform an analysis upon. For the simulations under wind conditions, the wind model was set to 2.7m/s (15% of flight speed) and a severity of 10 −1 using the Dryden −q + r sequence. The landing accuracy is colour-coded according to the physically required specifications given in Table 3.1, where landings within the inner bounds are marked green, landings between the inner and outer bounds are marked yellow, and landings outside of the outer bounds are marked red. To analyse these touchdown points, the statistical mean and standard deviation are used as symbols p t and σ t , respectively.
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Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle

Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle

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Modeling and Autonomous Flight Simulation of a Small Unmanned Aerial Vehicle

Modeling and Autonomous Flight Simulation of a Small Unmanned Aerial Vehicle

A flight simulator is a system that attempts to imitate the experience of flying an aircraft as practically as possible. The types of flight simulators vary from personal computer video games to full size cockpit replicas controlled by sophisticated computer systems. Flight simulators are widely used by academic researchers, the aviation industry, and air forces for pilot training and aircraft development.

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Actual Entities: A Control Method for Unmanned Aerial Vehicles

Actual Entities: A Control Method for Unmanned Aerial Vehicles

The focus of this thesis is on Actual Entities, a concept created by the philosopher Alfred North Whitehead, and how the concept can be applied to Unmanned Aerial Vehicles as a behavioral control method. Actual Entities are vector based, atomic units that use a method called prehension to observe their environment and react with various actions. When combining multiple Actual Entities a Colony of Prehending Entities is created; when observing their prehensions an intelligent behavior emerges. By applying the characteristics of Actual Entities to Unmanned Aerial Vehicles, specifically in a situation where they are searching for targets, this emergent, intelligent behavior can be seen as they search a designated area and locate specified targets. They will alter their movements based on the prehensions of the environment,
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