Top PDF Actual Entities: A Control Method for Unmanned Aerial Vehicles

Actual Entities: A Control Method for Unmanned Aerial Vehicles

Actual Entities: A Control Method for Unmanned Aerial Vehicles

Swarm intelligence has been observed in school of fish, swarms of different insects, and flocks of birds. As Krause [5] points out, this group living does enable problems to be solved that would be impossible or extremely hard for an individual from one of these groups. These behaviors have been studied and the ability that the groups have of solving cognitive issues larger than what one individual could facilitate has become known as swarm intelligence (SI). This term was coined by Beni and he used it to describe the organization of simple agents using nearest neighbor interaction [1]. Individual entities in SI are not sophisticated on their own; however, when their actions are combined more intricate tasks can be done. Each tends to have its own set of simple functions that it can perform. The SI is an emergent behavior that comes from these simple functions being done by multiple individuals and causes the appearance that they are all communicating and working together. The definition proposed by [5] for SI is “two or more individuals independently, or at least partially independently, acquire information and these different packages of information are combined and processed through social interaction, which provides a solution to a cognitive problem in a way that cannot be implemented by isolated individuals.”
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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

A data-driven control method for designing attitude control law of fixed-wing UAV is thus developed. The control system is divided into two cascade subsys- tems, including an inner loop system for angular velocity control and an outer loop system for Euler angle control. As the angular velocity control system con- tains all model information (certainties and uncertainties), a novel data-driven MFAC [13] [14] method is adopted for the inner loop angular velocity control law design. MFAC has been wildly used in some fields [15] [16], but hardly in aeronautic field. As we know of, only one paper [17] studied the application of MFAC to design control law for tracking horizontal trajectory of fixed-wing UAV, but it took no consideration of attitude control, which is an unneglectable problem. The method developed in our paper uses no model information, but only I/O data of UAV to obtain control law by optimizing the deflection angles of con- trol surfaces in real-time. The model of outer loop control system, which is uni- versal and contains no uncertainty, depicts the kinematic relationship between Euler angel and angular velocity. Thus the IMC method, which was hardly used in fixed-wing UAV application but widely in other fields [18] [19] [20], is used to de- sign outer loop Euler angle control law. IMC based controller is featured with less tuning parameters and simple tuning process, for example, only one parameter needs to be tuned for each channel of the roll, pitch, and yaw Euler angles.
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Robust Cooperative Close Formation Flight Control Of Multiple Unmanned Aerial Vehicles

Robust Cooperative Close Formation Flight Control Of Multiple Unmanned Aerial Vehicles

So far, the close formation control problem has been studied using different methods, such as PI controller [7], sliding mode control [8], LQR control [9], MPC control [10], adaptive control [11], and robust control [12]. However, all of them were developed under the leader-follower architecture, and there was no cooperation between two UAVs. The efficacy of the existing methods can only be guaranteed for close formation flight of two or three aircraft. Increase of formation size (number of UAVs) will result in dramatic loss of efficiency and accuracy for the existing methods. To deal with the deficiency of the existing methods in close formation flight of more than three UAVs, a cooperative control method is proposed in this paper. A bidirectional communication topology is employed. UAVs in close formation are required to communicate with some of their neighbors. To enhance the robustness against model uncertainties and formation aerodynamic disturbances, the uncertainty and disturbance estimation technique is employed and combined with the proposed cooperative formation controller. The efficiency of the proposed control algorithm is verified via the close formation simulation of five aircraft.
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Online system identification for fault tolerant control of unmanned aerial vehicles

Online system identification for fault tolerant control of unmanned aerial vehicles

Another method explored to obtain angle-of-attack measurements, was through the estimation. 2 different models were looked at and discussed. The first being based on the short-period mode dynamics of the aircraft and the second being a model based on the kinematic relationship of the different measured variables. The main downfall of the method based on the short-period mode dynamics, is that the accuracy of the angle-of-attack measurement is dependent on the aircraft parameters. In a Fault Tolerant Framework, the aircraft parameters are assumed to be varying and not known exactly. In this case the estimated angle-of-attack, as determined using the short-period model, will not be accurate. The second approach to estimating the angle-of-attack that was considered was based on the FPR method presented previously. Here the estimated angle-of-attack was based solely on the measured aircraft states. This method was seen to be more robust, when applied in a Fault Tolerant Framework and was thus chosen.
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A method for using unmanned aerial vehicles for emergency investigation of single geo-hazards and sample applications of this method

A method for using unmanned aerial vehicles for emergency investigation of single geo-hazards and sample applications of this method

Abstract. In recent years, unmanned aerial vehicles (UAVs) have become widely used in emergency investigations of major natural hazards over large areas; however, UAVs are less commonly employed to investigate single geo- hazards. Based on a number of successful investigations in the Three Gorges Reservoir area, China, a complete UAV- based method for performing emergency investigations of single geo-hazards is described. First, a customized UAV sys- tem that consists of a multi-rotor UAV subsystem, an aerial photography subsystem, a ground control subsystem and a ground surveillance subsystem is described in detail. The im- plementation process, which includes four steps, i.e., indoor preparation, site investigation, on-site fast processing and ap- plication, and indoor comprehensive processing and applica- tion, is then elaborated, and two investigation schemes, au- tomatic and manual, that are used in the site investigation step are put forward. Moreover, some key techniques and methods – e.g., the layout and measurement of ground con- trol points (GCPs), route planning, flight control and image collection, and the Structure from Motion (SfM) photogram- metry processing – are explained. Finally, three applications are given. Experience has shown that using UAVs for emer- gency investigation of single geo-hazards greatly reduces the time, intensity and risks associated with on-site work and
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Fault Diagnosis and Fault-Tolerant Control of Unmanned Aerial Vehicles

Fault Diagnosis and Fault-Tolerant Control of Unmanned Aerial Vehicles

means of the so-called residuals, which can be generated in different ways: parity equations, state estimation-based methods, and parameter estimation-based methods. The performance of model- based methods depends strongly on the usefulness of the constructed model [73]. The constructed model must include all situations under study. It must be able to handle changes at the operat- ing point. If the constructed model fails, the whole diagnostic system will also fail. However, in practice, it is usually quite challenging and difficult to meet all the requirements of model-based techniques due to the inevitable un-modeled dynamics, uncertainties, model mismatches, noises, disturbances, and inherent nonlinearities [74]. The sensitivity to modeling errors has become the key problem in the application of model-based methods. In contrast, data-driven diagnosis ap- proaches, such as neural network-based intelligent methods, mostly rely on historical and current data from the sensors, and do not require a detailed mathematical model of the system but need representative training data. The idea is that the operation of the system is classified according to measurement data. Formally, this is a mapping from measurement space into decision space [73]. Therefore, data play a very important role in this kind of method. With application to UAVs, model-free neural network-based approaches are preferred due to the cost limitation and short pe- riod of development compared to conventional aircraft. The capabilities of neural networks for function approximation, classification, and their ability to deal with uncertainties and parameter variations make them a viable choice for using in FDD problems [75].
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Analysis of Model-Aided Navigation of Unmanned Aerial Vehicles

Analysis of Model-Aided Navigation of Unmanned Aerial Vehicles

The Flight Simulator can be controlled by a joystick or by a Matlab script containing the pilot commands. This script control feature is contained in the main input script file that contains the aircraft aerodynamic coefficients, mass and inertia properties; this same script is able trough a variable to consider or not windy conditions and eventually to modify the wind input parameters. In-board sensors as Pitot and Vanes are simulated perturbing the 6Dof solutions with random noise and errors. All the available output variables are then stored in a single data files labeled according the actual date. Five-minutes length flights has been performed with this simulator in order to imitate the real flights.
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Fault tolerant flight control system design for unmanned aerial vehicles

Fault tolerant flight control system design for unmanned aerial vehicles

The initial condition is another factor which must be considered in the design and selection of the controller. The sensitivity of the starting point on the overall solution is critical particu- larly in the case on linear techniques. This sub-section investigates the difference between the solutions produced by the linear and nonlinear controllers as a function of the initial condition. Again the path is y = 5 and the initial y is varied from 0m to 10m in steps of 0.1m. The errors between the nominal path and the actual robot position are calculated at various points along the prediction horizon namely at 1 sec, 2 secs, 3 secs, 4 secs and 5 secs for all initial y values. Plots of errors versus initial y for the different time are given in figures 3.76 to 3.80. The plots given in these figures show the errors between the optimal solution and the nominal path as well as the errors between the integrated solution and the nominal path for both the linear and nonlinear controllers. The errors arising from the integrated solution of the linear controller are shown on a separate plot underneath the main plots as the errors were much higher compared to the others and by plotting all errors on the one graph the errors produced by the other solutions were not as clearly visible. The results show that as the time increases from 1 second to 5 secs the errors decrease as the robot approaches the nominal path. The results clearly show that the further away the robot is from the nominal path (i.e. the greater the perturbation) the higher the error in the case of the linear controller. At the 1 second mark along the prediction window (figure 3.76) the errors between the solution produced by the nonlinear controller and the nominal path (y = 5) are seen to be linear as a function of initial y. Moving further along the prediction window (figures 3.77, 3.78, 3.79 and 3.80) shows that these errors decrease and are very close to zero for any y 0 . There is only a small region around the nominal path, y = 5,
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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

In order to minimise risk to the aircraft, extensive Hardware In the Loop (HIL) simulations were run to ensure the satisfactory operation of the system. These simulations use the flight ready avionics and connects it to a simulation environment that emulates the motion of the physical airframe and sends dummy sensor data to the avionics. Since the avionics cannot tell the difference between real and dummy data, this test emulates actual flight with a high degree of accuracy. The HIL simulation that was used has been developed in the ESL in Simulink (as part of the MATLAB® software package).
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Observing river stages using unmanned aerial vehicles

Observing river stages using unmanned aerial vehicles

We carried out the UAV survey using the swinglet CAM fixed-wing solution manufactured by senseFly. Swinglet CAM is lightweight (0.5 kg) and its payload includes a single consumer-grade RGB camera that records the photographs as JPG files. The individual pictures are geotagged. In or- der to produce orthophotomaps we process these files with the structure-from-motion (SfM) algorithm (Westoby et al., 2012) in the Photoscan software provided by AgiSoft, with- out use of ground control points (GCPs). We produced the georeferenced orthophotomaps in Photoscan, which for the purpose of georeferencing uses coordinates extracted from the geotagged images. Such orthophotomaps were compared with the lidar digital terrain model (DTM), and we identi- fied offsets between the two. The resolution of the lidar data was of 1 m, and the data acquisition was carried out in 2010. To remove the offsets we used the spline function in Ar- cMap 10.2.2 by ESRI. We identified characteristic features in the lidar DTM, which were evenly distributed and possible to identify in the orthophotomap. These features comprise: crossings of bounds, crossings of drainage ditches, and cen- tres of bridges or passages (crossings of streams and roads). More than 10 points were used to perform georeferencing, as the spline method requires. The spline function allowed us to precisely georeference the control points (i.e. the afore- mentioned mutual features) and transform raster data sets
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An Optimal Control Approach to Flight Management

Systems for Unmanned Aerial Vehicles

An Optimal Control Approach to Flight Management Systems for Unmanned Aerial Vehicles

Lloyd’s algorithm [60] [31] provides a method to find CVTs. The algorithm is performed itera- tively, by computing the Voronoi diagram and moving each generator towards the center of mass of their respective Voronoi cell. Knowledge of the movement of the center of mass of Voronoi cells may prove useful in future research into coverage problems, for example the movement of the cen- ter of mass of Voronoi cells will be needed in section 4.4 of this thesis. This section will present the change in position of the center of mass of a Voronoi cell using both geometric analysis and Reynolds Transport Theorem.
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A survey on fractional order control techniques for unmanned aerial and ground vehicles

A survey on fractional order control techniques for unmanned aerial and ground vehicles

A new method to obtain a flat phase margin with FOPID controller is proposed [118]. This method is applied for the roll control of a UAV using the specifications: gain margin, phase margin and robustness to gain variations of the system with two different approaches. The first approach consists in a limited number of frequency samples of G(s) around of its crossover frequency for the controller design. This approach allows greater computational efficiency than the classical method, which uses a first order plus time delay (FOPTD) model. In the second approach, an approximated open loop system (G 0 (s)) with the same amplitude of G(s) and different phase curve is used. Moreover, two additional controllers are used: an IOPID controller and a classical FOPID. For the performance evaluation of the controllers, uncertainties in the aerodynamic parameters of the UAV are added. The simulation results in MATLAB demonstrate that the new FOPID controller is more robust than the other controllers in closed loop.
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Time-critical cooperative path-following control of multiple unmanned aerial vehicles

Time-critical cooperative path-following control of multiple unmanned aerial vehicles

The coordinated path-following control problem was implicit in the early work in [ 33 ], where the authors built on and extended the single-vehicle “manoeuvre regulation” approach in [ 45 ], and presented a solution to the problem of coordinated operation of an autonomous surface vehicle and an autonomous underwater vehicle. The strategy adopted, however, requires the vehicles to exchange a large amount of information, and cannot be easily generalized to larger teams of vehicles. These drawbacks were later overcome in [ 60 ], which proposes a leader-follower cooperative approach that (almost) decouples the temporal and spatial assignments of the mission. The solution adopted is rooted in the results on path-following control of a single vehicle presented in [ 95 ], and takes advantage of the fact that, with this path-following algorithm, the speed profile of each vehicle becomes an additional degree of freedom that can be exploited for vehicle coordination. Moreover, in this setup, the two vehicles only need to exchange the (scalar) “along-path positions” of their virtual targets, which reduces drastically the amount of information to be exchanged among vehicles when compared to the solution developed in [ 33 ]. Interestingly, an approach similar to the one in [ 60 ] was proposed at approximately the same time in the work in [ 92 ] and [ 93 ], where a nonlinear control design method was presented for formation control of a fleet of ships. The approach relies on the maneuvering methodology developed in [ 94 ], which is then combined with a centralized guidance system that adjusts the speed profile of each vehicle so as to achieve and maintain the desired formation configuration. The maneuvering strategy in [ 94 ] was also exploited in [ 46 ], where a passivity framework is used to solve the problem of vehicle coordination and formation maneuvering.
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Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles

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

The task of flight control design is to form a feedback loop to maintain the aircraft states and/or to drive some of the outputs (e.g. airspeed and altitude) to specified values. In conventional feedback control design, since the wind components and the force/moment disturbances are unknown to the controller, the actual control performance will be degraded because of their adverse effects. To improve flight performance for small UAVs in windy conditions, this study adopts a two-step approach to take into account the disturbances in flight control design. Specifically, unknown disturbances are first estimated based on their effects on the nominal UAV dynamics. Then, those estimates are incorporated in control design to compensate the influences due to disturbances.
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A Review on Geo Mapping with Unmanned Aerial Vehicles

A Review on Geo Mapping with Unmanned Aerial Vehicles

The design of flight paths is an important component of UAV mapping. This is typically done using software packages; many drone manufacturers offer proprietary software with their drones. Mission Planner, an open-source software package, is the single most widely used solution. The functionality of several competing software packages is broadly similar. UAV mapping missions are usually flown in a specific pattern of parallel lines, commonly described as “transects,” which are connected to a series of “waypoints”—think of a connect-the-dots pattern of parallel lines, or the pattern in which you might mow the lawn. A transect flight pattern is a method of ensuring that the UAV captures an adequate quantity of images that overlap to the degree required for the processing software to create a high-quality and accurate map. For maximum quality, some UAV mappers suggest flying two different overlapping patterns over the same area but at different heights. This method collects a large quantity of data and helps to resolve elevation variation problems, which result when tall geographic features throw off the scale of the rest of the image. Others recommend adjusting the altitude of the drone to keep a constant altitude above ground level, even as features on the ground vary in altitude. To create a flight plan with transects using current software such as Mission Planner, the pilot first connects with the UAV’s flight controller via either a ground control radio attached by USB cable to a computer or tablet, or a direct USB link from the UAV to the computer. (Flight plans can also be generated on the computer and uploaded to the flight controller later). The pilot opens the software and defines an area to be mapped with a polygon, then specifies the camera model, the desired operational altitude, and how the camera will be triggered to take photographs. Once these factors are entered, Mission Planner generates
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Unmanned aerial vehicles: potential tools for use in zoonosis control

Unmanned aerial vehicles: potential tools for use in zoonosis control

Unmanned aerial vehicles (UAVs) have become useful tools to extend human abilities and capacities. Currently UAVs are being used for the surveillance of environmental factors related to the transmission of infectious diseases. They have also been used for delivering therapeutic drugs and life-saving supplies to patients or isolated persons in extreme conditions. There have been very few applications of UAVs for disease surveillance, control and prevention to date. However, we foresee many uses for these machines in the fight against zoonotic disease. The control of zoonoses has been a big challenge as these diseases are naturally maintained in animal populations. Among 868 reported zoonoses, echinococcosis (hydatid disease) is one of the most severe public health problems and listed as one of 17 neglected tropical diseases targeted for control by the World Health Organization. Infected dogs (domestic or stray) play the most important role as definitive hosts in maintaining the transmission of echinococcosis. However, the actual contribution of wild canines to transmission has received little attention as yet, but should certainly not be ignored. This paper summarizes the history of development and application of UAVs, with an emphasis on their potential use for zoonosis control. As an example, we outline a pilot trial of echinococcosis control in the Qinghai-Tibet Plateau region, in which UAVs were used to deliver baits with praziquantel for wildlife deworming. The data suggested that this is a cost-effective and efficient approach to the control of zoonotic diseases transmitted among wild animal populations.
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Robust gain-scheduled H∞ control for unmanned aerial vehicles

Robust gain-scheduled H∞ control for unmanned aerial vehicles

An LPV plant model was first introduced by Shamma and Athans [91] whereby its dynamic characteristics vary, following some time-varying parameters whose values are unknown a priori but can be measured in real-time and lie in some set bounded by known minimum and maximum possible values. An algebraic manipulation method, e.g. Jacobian linearization [39, 66, 80], state transforma- tion [13, 92], or function substitution [94], etc., is normally used to derive an LPV model from the original nonlinear model. Moreover, in the literature, there are sev- eral different varieties of LPV models, e.g. the grid LPV model [39, 66, 100, 101], the affine LPV model [8, 7, 10] (or polytopic LPV model), the tensor-product (TP) convex polytopic model [15, 16, 18], etc., these have been introduced for the analysis and gain-scheduled control synthesis which is usually based on sin- gle quadratic Lyapunov function [10, 23] or parameter-dependent Lyapunov func- tions (e.g. parameter-dependent [8, 39, 100, 101], affine parameter-dependent [45], piecewise-affine parameter-dependent [63, 64], blending parameter-dependent [94], multiple parameter-dependent Lyapunov functions [65, 66], etc.).
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Teleimpedance control of unmanned aerial vehicles

Teleimpedance control of unmanned aerial vehicles

It is proposed that, from human arm motion, force and inertial data is measured, impedance of the human arm is approximated through stiffness estimation, and replicated on the slave site. The human operator experiences a force feedback giving information on the force working on the end- effector, when in contact with unknown surroundings. Vibrotactile feedback will be used as force feedback. Previous research, e.g. by Cheng et al, has shown that humans are well capable of relating a certain vibrational pattern to an actual value of interest [5].
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Verifiable control of a swarm of unmanned aerial vehicles

Verifiable control of a swarm of unmanned aerial vehicles

Although the artificial potential function method is theoretically elegant, Sigurd [29] points out that the assumption that all UAVs have information on the position of all other UAVs in the system is unrealistic as the number of UAVs increase. Each UAV will now have a sensing region that will ensure collision avoidance and an equally spaced final formation as shown in Eq. 55 and Fig. 11, where Z r is the radius of repulsive zone of influence;

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DESIGN OF H INFINITY CONTROLLERS FOR UNMANNED AERIAL VEHICLES

DESIGN OF H INFINITY CONTROLLERS FOR UNMANNED AERIAL VEHICLES

The objective is to provide efficacious methods for the design of flight controllers for remotely piloted helicopters, which have guaranteed performance and prescribed multivariable loop structures. The problem of stabilization of an autonomous helicopter in hover configuration subject to external disturbances is addressed. When the problem involves dynamic constraints, a simplified output- feedback (OPFB) design procedure is employed to obtain the desired performance. An efficient algorithm is taken to evaluate OPFB gains, which do not require initial stabilizing gains for computation. Helicopter dynamics do not dissociate and hence the design of the flight controllers with an intuitive and desirable structure is ambiguous. Shaping filters are added that improve the performance, yield guaranteed robustness and speed of response. The salient feature of design is that it does not include the presence of noise, however, it has been verified that the control is an efficient method for controlling of unmanned helicopters in the presence of noise and robustness of the design has been verified by taking different real time uncertainties. Also it has been observed that has performed its control faster with reasonable accuracy.
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