Top PDF A software-in-the-loop implementation of adaptive formation control for fixed-wing UAVs

A software-in-the-loop implementation of adaptive formation control for fixed-wing UAVs

A software-in-the-loop implementation of adaptive formation control for fixed-wing UAVs

    Abstract — This paper discusses the design and software-in-the- loop implementation of adaptive formation controllers for fixed- wing unmanned aerial vehicles (UAVs) with parametric uncer- tainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission (e.g. depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on Ardu- Pilot, a popular open-source autopilot suite. Specifically, the Ar- duPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV form- ations while handling uncertain mass and inertia.
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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

The attitude control of fixed wing UAVs is studied based on the characteristics of inner loop and outer loop control systems, and realized by using MFAC to design the control law of inner loop angular velocity system and IMC to design that of Euler angle system. Firstly, the MFAC based controller has achieved in- ner loop angular velocity control using only I/O data without any model infor- mation. The IMC based controller has realized outer loop Euler angle control using a few tuning parameters (only one for each channel) and easy tuning process. Secondly, compared with conventional model-free CPID method and detailed model-based NDI method, it is discovered that the method developed is capable of dealing with strong coupling and highly nonlinear system such as that of fixed-wing UAVs; and it shows superiority in dealing with disturbances.
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Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Copyright © 2016 Caleb Rice et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named “Phastball” and a set of quadrotors named “NEO.” Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.
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Trinocular Ground System to Control UAVs

Trinocular Ground System to Control UAVs

The Computer Vision Group at the UPM (Universidad Politcnica de Madrid) counts with three fully operational UAV systems to develop indoors and outdoors applications. Such systems form part of the COLIBRI Project, whose purpose is to develop algorithms for vision based control of UAVs [15]. The experiments were carried out with the Colibri III system (see Fig. 1). It is a Rotomotion SR20 helicopter with an electric motor of 1.300 W, 8A. It is equipped with an xscale-based flight computer, a GPS, an IMU (Inertial Measurement Unit), an onboard pan and tilt camera, and an onboard Nano Itx computer for image processing algorithms. The flight computer runs Linux OS and uses an 802.11g wireless Ethernet protocol for sending and receiving information to/from the ground station. B. Trinocular System
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Attitude Estimation and Control of VTOL UAVs

Attitude Estimation and Control of VTOL UAVs

To develop the control laws, we first propose a new attitude extraction algorithm (which extracts the desired attitude corresponding to a desired translational accel- eration) in terms of the unit-quaternion that has almost no restrictions on the de- manded acceleration, except for a mild singularity that can be avoided. Relying on this quaternion extraction method, we present two adaptive tracking controllers using the torque (that is applied to the system rotation dynamics) as a control input. Both controllers depend on an adaptive estimation method, which uses a projection mecha- nism Ioannou and Sun (1996), Cai et al. (2006). The projection mechanism is required to avoid the singularity associated with the value of the system thrust, which as a result, is guaranteed to be non-vanishing. The first proposed controller achieves the position-tracking objective for any initial condition of the state, whereas the second controller achieves the position-tracking objective for a set of initial conditions which are dependant on the control gains. The latter controller is included since it is less complicated than the prior case and may be more suitable to use in practice. During the process of developing the two control laws, the disturbance forces are assumed to be constant in the inertial frame. In this case, both control laws are proven to achieve the position tracking objective provided that an upper bound of the disturbance force is known a-priori (although the actual magnitude of this disturbance force may be less than this limit). The results for this work have been reported in Roberts and Tayebi (2011a).
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Slipstream Deformation of a Propeller-Wing Combination Applied for Convertible UAVs in Hover Condition

Slipstream Deformation of a Propeller-Wing Combination Applied for Convertible UAVs in Hover Condition

Convertible unmanned aerial vehicle (UAV) promises a good balance between convenient autonomous launch/recovery and efficient long range cruise performance. Successful design of this new type of aircraft relies heavily on good understanding of powered lift generated through propeller-wing interactions, where the velocity distribution within propeller slipstream is criti- cal to estimate aerodynamic forces during hover condition. Current study analysed a propeller- wing combination with a plain flap. A 5-hole probe measurement system was built to construct 3 dimensional velocity field at a survey plane af- ter trailing edge. The study has found that sig- nificant deformation of propeller slipstream was present in the form of opposite transverse dis- placement on extrados and intrados. The defor- mation could be enhanced by flap deflections. Velocity differences caused by the slipstream de- formation could imply local variation of lift dis- tribution compared to predictions from conven- tional assumptions of cylindrical slipstream. The research underlined that the mutual aspect of propeller-wing interaction could be critical for low-speed aerodynamic design.
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Robust Fault Diagnosis for Fixed Wing Aircraft Fault Tolerant Flight Control System

Robust Fault Diagnosis for Fixed Wing Aircraft Fault Tolerant Flight Control System

FTFC is used to recover the faulty aircraft during the occurrence of fault. FTFCs are classified as active and passive based on the application of FDI in the FTFC. Active FTFC consist of additional FDI scheme to estimate fault and operate whereas passive FTFC operates without FDI. Most of the FTFC are functioning without FDI and employ a combination of different control methods. FTFC is developed by comparing achieved and demanded aircraft altitude [1]. Authors applied FTFC on twin engine unmanned aircraft and successfully recovered faulty aircraft for predefined fault scenario. It was also explained that the absence of FDI limits the functionality of FTFC. The damaged aircraft is experimented in a wind tunnel to identify the parameters during physical damage of the aircraft. [2], [3]. Authors developed FTFC based on the reallocation of normal
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Neural mechanisms of adaptive gain control in a joint control loop: muscle force and motoneuronal activity

Neural mechanisms of adaptive gain control in a joint control loop: muscle force and motoneuronal activity

parameters such as amplitude, frequency or velocity. However, the presynaptic gain control mechanism that acts on the fCO afferents may also contribute to these characteristics (Burrows and Laurent, 1993; Burrows and Matheson, 1994; Wolf and Burrows, 1995). The decrease in gain found during repetitive fCO stimulation and the subsequent spontaneous recovery might derive partially from sensory adaptation. Habituation and dishabituation (e.g. by tactile stimuli) demand an interaction between interneuronal pathways that receive convergent information from proprioceptive and exteroceptive sense organs (Burrows, 1989; Laurent and Burrows, 1989). These characteristics have been shown for many of the nonspiking interneurones of the FT feedback system (Schmitz et al. 1991; Büschges et al. 1994; Kittmann et al. 1996). They might change the balance of the antagonistic pathways in the parallel, distributed circuitry that has been demonstrated to underlie the feedback system (Büschges and Schmitz, 1991; Büschges, 1990, 1995; Sauer et al. 1996). Furthermore, results indicate that a tonic, unmodulated activation or inhibition of the extensor or flexor motoneurones could act as an effective mechanism of gain control. Neuromodulators such as octopamine act on the sensory cells (Ramirez et al. 1993) and on central neurones of the circuit and might also contribute to gain control, especially with regard to the behavioural state (inactive, active) of the stick insect (Büschges et al. 1993). To determine where the structures involved in gain control are located and what mechanisms they use, further intracellular investigations are necessary. Such studies will have to establish the relevance of the mechanisms described above under stimulus conditions in which behaviourally relevant changes in gain occur (locust: Büschges and Wolf, 1996). In particular, the idea that there might be two different types of neuronal pathway, both important in gain control, will have to be considered in further experiments: pathways that carry fCO stimulus-modulated information and change their strength during gain control (mechanism i, Fig. 13) and pathways that do not carry fCO stimulus-modulated information but receive a tonic activation or inhibition during changes in gain (mechanisms ii and iii, Fig. 13).
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Tracking objects with fixed-wing UAV using model predictive control and machine vision

Tracking objects with fixed-wing UAV using model predictive control and machine vision

When designing GUIs and HMIs it is extremely important to make the architecture hierarchy wide and not high. This is because modularized, wide implementations easily support integration of improved functionality or new features without any huge changes in the HMI’s core. The Qt platform introduces a implementation philosophy which is different opposed to regular C++ implementations. By using the qml tool the main thread in C++ is dedicated to run the logic defined in the qml hierarchy that constitutes the graphical user interface (GUI), which is the graphical part of the HMI. In addition, classes in C++ can be implemented to provide information to the qml hierarchy. An example of such a class could be a class which includes MPC parameters and limits, which are managed by get and set functions. This class’ member functions will not run continuously in a regular state machine or dedicated threads. Instead functions calls from the qml hierarchy is made to interface the class and its information. If work should be done in a periodic manner, as done in regular threading mechanisms, the class should include sub-classed thread classes which should be started once the operator requests such mechanisms to run. One example of such a periodic tasks could be the communication mechanisms which enables a communication channel between the HMI and the engine running the MPC. In this chapter we will use the term Graphical User Interface (GUI) meaning the qml hierarchy which defines the HMI’s layout. It should be clear that the GUI represents the graphical part of the total system, the HMI. Before discussing any more details regarding the system specifications listed in the previous section we start by designing the communication channel, which is essential in the HMI’s core.
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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

As UAVs are well suited for use as surveillance platforms, they are usually fitted with a camera which allows a remote user to gain visual information about the surrounding envi- ronment and increase situational awareness. Such UAVs are currently being used to perform a variety of surveillance tasks, which include land surveys and patrolling of borders or coast- lines. During these surveillance missions, there are situations when a vehicle, person or object is spotted and needs to be inspected or followed. Since this is not a preplanned objective, the navigation needs to be implemented while in flight with limited information about the object. This is called Dynamic Pursuit Navigation and allows the onboard camera has to be positioned relative to the object by using real-time data to enable an unobstructed line of sight. This part of the thesis focusses on the development of the intermediate guidance control and a Dynamic Pursuit Navigation algorithm.
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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

This work aims to improve the flight performance of small fixed-wing UAVs in wind conditions by designing an advanced flight controller based on the available aircraft model. In particular, the control design focuses on the longitudinal dynamic model, because it is important for taking off and landing of such a UAV in the presence of wind disturbances [6, 7]. General longitudinal aircraft dynamics possess strong nonlinearities and uncertainties, which has necessitated the use of nonlinear control methods. Robust nonlinear dynamic inversion (NDI) has been applied to control the longitudinal dynamics of a hypersonic aircraft [8]. An adaptive sliding mode control was developed later to tackle the same problem [9], which also considered the case that only a part of the aircraft states is measurable. In recent years, robust adaptive control techniques have been applied to the longitudinal channel of air-breathing hypersonic vehicles with flexible structures and non-minimum phase behaviors [10, 11]. In terms of small UAVs, reference [12] has proposed a novel adaptive backstepping method to tackle the system uncertainties as well as thrust saturation.
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A comparison of fixed final time optimal control computational methods with a view to closed loop implementation using artificial neural networks

A comparison of fixed final time optimal control computational methods with a view to closed loop implementation using artificial neural networks

while applying the maximum­principle to the  same problem yielded two nonlinear ordinary  differential equations (32) and (35). Although  both  techniques  generate  equations  which  require  the  aid  of  digital  computers  for  solution,  the  two  first­order  ordinary  differential  equations  from  the  maximum­  principle  are  considered  easier  to  solve  than  the  nonlinear  partial  differential  equation. In  general,  therefore  Pontryagin’s  method  is  recommended, but Bellman’s  method should  still  be  considered  as  in  particular  cases  it  might prove to be more straightforward.  In order to determine the attainable  closeness  to optimality, it is recommended that the new  scheme depicted in Fig. 2 is  first tested with  very  simple  well  known  cases  for  which  exact closed loop optimal controllers already  exist, such as the double integrator plant. The  next  recommended  step  is  to  test  out  the  scheme  depicted  in  Fig  2  for  the  simple  minimum  energy  control  problem  presented  in section 4. The variation of the closeness to  optimality with the number of training points  and  their  distribution  within  the  operating  envelope of the plant should be investigated.  7. References: 
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The design of adaptive structures for wing morphing

The design of adaptive structures for wing morphing

Graphical methods are employed here by means of residuals plots, initially based on the assumption of constant standard deviation across the data. The method employed for such assumption is an ordinary least squares regression (OLS) to determine the pa- rameter estimates (see Appendix C for more details). A potential pitfall of using OLS is that the variation trend exhibited by the residuals might not model the determin- istic part of the data accurately, leading to a changing variance across the model. In such cases, the residuals won’t follow an evenly spread trend, but will emphasise the presence of the outliers, that can bias the prediction and alter the parameter estimates. Therefore, a weighted procedure is also employed by means of iteratively reweighted least squares (IRLS) (Rousseuw and Leroy (1987)) on the assumption of non-constant standard deviation. The outliers reside in inconsistencies with the bulk data and can dominate the regression, but, although if dropped can increase the correlation between the independent and dependent variables, the outliers may also contain engineering in- terpretation about the data under investigation and, therefore, ideally should not be removed. The source of the outliers might be partly converged solutions (i.e., structural or aerodynamic in nature) and can be repaired by making use of high-fidelity analysis tools (see Kim (2001)). The negative impact of the outliers on the regression plane can be alleviated by using the IRLS method, as it assigns different levels of quality to data through quantitative means of weights to control the contribution of each observation to the parameter estimates. The advantage of using IRLS over a number of hybrids of OLS (e.g., IRLS with Huber weighting technique, univariate outlier and multivariate outlier removal) is also emphasised by Wager et al. (2005) in a neuro-imaging study on hemo- dynamic shapes, achieving robust parameter estimates with artificial influential outliers, iteratively down-weighted onthe principles of DuMouchel and O’Brien (1989) (i.e., the residuals are standardised with respect to the median absolute deviation, technique that is also used in the current work, see Appendix C for the numerical approach).
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NEW ZEALAND FIXED WING AVIATION ACCIDENTS

NEW ZEALAND FIXED WING AVIATION ACCIDENTS

Other factors such as the time of day and day of week for accident occurrences were less conclusive. Saturday was the most frequent day for fixed wing accidents while Friday was most common for fatal accidents. Midday to mid afternoon was the most common time for accidents with most fatal accidents occurring between 1400 and 1500 local time. No clear explanation can be given for this trend, although end of week fatigue and increased private flying during weekends may have played a part. Revenue flights had higher numbers of accidents midweek and Friday whereas the majority of non-revenue accidents occurred at weekends.
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Design and Optimization of Wing Structure for a Fixed Wing Unmanned Aerial Vehicle (UAV)

Design and Optimization of Wing Structure for a Fixed Wing Unmanned Aerial Vehicle (UAV)

A straight beam wing is mainly consisted of skin, wing rib and wing beam. Skin forms a streamlined outer surface of the wing that take part in the air- frame’s force. In order to ensure the smoothness of the surface, the skin should have sufficient lateral bending stiffness. The skin can be combined with the stringer to form an integral panel to bear normal stress. The function of wing rib is to form the shape of the wing profile. It is connected to the truss and skin and provides vertical support to the truss and skin in the stiffness of its own plane. Wing beam is a simple load bearing structure that can support shear force Q and bending moment M [9]
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Application Of Reinforcement Learning In Heading Control Of A Fixed Wing UAV Using X-Plane Platform

Application Of Reinforcement Learning In Heading Control Of A Fixed Wing UAV Using X-Plane Platform

An unmanned Aerial vehicle, UAV is a space traversing vehicle that flies without a human crew on board. UAVs can be remotely controlled, semi-autonomous, autonomous or a combination of these. They are the future of aerial vehicles and they present an area of great interest to the control engineering fraternity. UAVs have a wide range of applications including surveillance, search and rescue, target tracking, digital mapping and weather observations. To accomplish these autonomous missions, it is essential to have a reliable heading control i.e. lateral direction control, thus an autopilot system is used. Autopilots were first developed for missiles but later extended to aircrafts and ships. Due to the nonlinearity of the system dynamics and parameter uncertainty in UAVs, several control techniques including PID control [1] [2], where two PID controllers were used in tandem, for the lateral and longitudinal motions. In [3] H ∞ control strategy was used. Adaptive control
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Implementation Of Double Closed-Loop Control System For Unmanned Ground Vehicles

Implementation Of Double Closed-Loop Control System For Unmanned Ground Vehicles

Abstract: The research work focuses on issues of vehicle modeling incorporating wheel-terrain interaction and low-level control design taking into account uncertainties and input time delay. Addressing these issues is of significant importance in achieving persistent autonomy for outdoor UGVs, especially when navigating on unprepared terrains. The vehicle is driven in the skid-steering mode, which is popular for many off-road land vehicle platforms. In this research work, a comprehensive approach is proposed for modeling the dynamics of UGV. The approach considers the difference in speed between two outputs of the differential and the turning mechanism of the vehicle. It describes dynamics of all components in the vehicle driveline in an integrated manner with the vehicle motion. Given a pattern of the throttle position, left and right braking efforts as the inputs, the dynamic behavior of the wheels and other components of the UGV can be predicted. For controlling the vehicle at the low level, PID controllers are firstly used for all actuators. As many components of the vehicle exhibit nonlinearities and time delay, the large overshoots encountered in the outputs can lead to undesirable vehicle behaviors. To alleviate the problem, a novel control approach is proposed for suppression of overshoots resulting from PID control. As a result, the proposed approach can improve significantly system robustness and reduce substantially step response overshoot. Notably, the design is generic in that it can be applied for many dynamic processes. Knowledge of the interaction between the UGV and the terrain plays an important role in increasing its autonomy and securing the safety for off-road locomotion. The novel interaction model takes into account the relationship between normal stresses, shear stresses, and shear displacement of the terrain that is in contact with the wheels in deriving the three-dimensional reaction forces. Finally, all modeling and control algorithms are integrated into a unique simulator for emulating the vehicle mobility characteristics. In particular, the wheel’s slip and rolling resistance can also be derived to provide useful information for closed-loop control when the UGV is navigating in an unknown environment.
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Fixed-Formation Control Based on Event-Driven of Multi-Agent System

Fixed-Formation Control Based on Event-Driven of Multi-Agent System

Abstract. In this paper, we study the formation control problem of the second-order multi-agent system, the so-called formation control problem is solved by designing decentralized control rate. All the agents are formed by autonomous movement to achieve a given formation. Considering that the multi-agent consumes in communication. In this paper, an event-driven control law protocol is proposed to reduce the number of multi-agent sampling times in the same time, and the sufficient conditions for the given formation to be satisfied in the range of the multi-agent system are given. Finally, the correctness and validity of the event-driven control protocol are verified by numerical simulation experiments.
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IMPLEMENTATION OF THE ADAPTIVE FILTER FOR VOICE COMMUNICATIONS WITH CONTROL SYSTEMS

IMPLEMENTATION OF THE ADAPTIVE FILTER FOR VOICE COMMUNICATIONS WITH CONTROL SYSTEMS

The correct determination of the length M of the adaptive filter is very important. When the length M of the adaptive filter is low, the speech signal processing as a result of a small number of parameters of the adaptive filter is inaccurate. High value of the adaptive filter length M lead to inaccurate speech signal processing by influence of the estimator variance increase.

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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

Traditional flight control applies a SISO approach to design the autopilot con- trollers for velocity and altitude separately. Throttle is then commanded de- pending on the difference in desired speed and actual speed, while the desired glide slope is commanding the elevator deflection. Such a controller will suffer from poor damping and overshooting when trying to perform certain coupled changes in altitude and speed. As an example of this one might consider an autopilot trying to change the glide slope and at the same time keep a constant velocity. As the flight path angle is changed, the aircraft gains speed. The con- troller now lowers the speed by changing the thrust, resulting in a reduced lift force and thus a change of the glide slope. The controller must therefore go through extensive testing in order to reach a design that minimizes these effects for the desired flight slope.
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