Dynamics Of Unmanned Aerial Vehicle Research Articles

Distributed Cooperative Onboard Planning for the Conflict Resolution of Unmanned Aerial Vehicles

The distributed cooperative onboard conflict resolution problem of unmanned aerial vehicles is studied in this paper. First, the dynamics of unmanned aerial vehicles and the safe separation constraint are studied. The monotonicity feature of the constraint condition is analyzed. The distributed conflict resolution problem is modeled as a distributed constraint optimization problem. Second, the distributed constraint optimization problem is solved in two steps. The feasible solutions are searched first, and the overall costs are minimized thereafter. The distributed stochastic gradient descent method is proposed to search the local near-optimal solutions. The self-improving mechanism is proposed to enhance the utilization efficiency of data exchanged among unmanned aerial vehicles. The “virtue adjustment” theorem is defined to motivate the cooperative coordination between unmanned aerial vehicles. Simulation results show that, compared with existing short-term algorithms, our algorithm could reduce the summation of additional flight distances and impacts on flight plans of unmanned aerial vehicles significantly and coordinate the flights of unmanned aerial vehicles in complex scenarios.

Trajectory-Driven Adaptive Control of Autonomous Unmanned Aerial Vehicles with Disturbance Accommodation

This paper presents the development of direct adaptive control laws to enable an autonomous unmanned aerial vehicle (UAV) to track reference trajectories in the presence of external disturbances. A maneuver database of reference trajectories is computed offline from a linear UAV dynamic model using an optimization routine. Each maneuver in the database is therefore represented as a state trajectory, as well as the control input sequence that generates that trajectory. The trajectory tracking control laws are designed to enable a UAV to follow reference trajectories from this maneuver database. A disturbance accommodating control law is first developed and applied for the tracking of reference trajectories while the UAV is subjected to a persistent external disturbance. This control design requires a state-space disturbance generator model and the derivation of ideal trajectories from a reference model, which requires the solution of a set of matching conditions. A direct adaptive controller is then developed to enable the UAV to track reference trajectories while subject to modeling error and external disturbances. The adaptive controller also requires a disturbance model and the existence of ideal trajectories, but it does not require the explicit solution of the matching conditions. Simulation results for the disturbance accommodating controller and the adaptive controller are presented using a linear six-degree-of-freedom dynamic model that is representative of typical UAV dynamics. The results show that, although both controllers track reference trajectories and provide disturbance rejection, the adaptive controller is better suited for handling modeling errors or uncertainties.

Automatic carrier landing control for unmanned aerial vehicles based on preview control and particle filtering

For the carrier-based unmanned aerial vehicles (UAVs), one of the important problems is the design of an automatic carrier landing system (ACLS) that would enable autonomous landing of the UAVs on a moving aircraft carrier. However, the safe autolanding on a moving aircraft is a complex task, mainly because of the deck motion and airwake disturbances, and dimension limitation. In this paper, an innovative ACLS system for carrier-based UAVs is developed, which is composed of the flight deck motion prediction, reference glide slope generation and integrated guidance and control (IGC) modules. The particle filtering method is used to online predict the magnitudes and frequencies of the deck motion, which are used to correct the reference glide slope to achieve minimum dispersion around the ideal touchdown point. An optimal preview control (OPC) scheme is presented for the IGC subsystem design, which fuses the preview information of the reference glide slope, equality constraint of UAV dynamics and performance index function, and predicted information of the carrier deck motion. Simulation results of a nonlinear UAV model show the effectiveness of the ACLS system in carrier autolanding under the deck motion and airwake disturbances.

Customizable Unmanned Aerial Vehicle Haptic Feedback Interface: Theory and Test

Unmanned aerial vehicles (UAVs) can be challenging to operate due to remote control environments and unstable system dynamics. A haptic control strategy is introduced to improve the UAV’s stability and facilitate customizable haptic feedback. The inverse kinematics of the haptic device are presented to map the workspace with the joint space of its motors. The mathematical model that describes the haptic device enables feedback forces to be calculated. A variety of feedback strategies are designed based on obstacle position, UAV dynamics, and the haptic device mechanical dynamics. Four haptic feedback methods are proposed and evaluated using human test subjects that operate the quadrotor over an obstacle-filled environment. The operators’ performances improved 27.2% in tracking error and 137% in time consumption as compared with the nonhaptic device. Clearly, the introduction of haptic feedback benefits the UAV operating paradigm.

Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes

Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the capability to autonomously detect potential landing sites in unknown and unstructured terrain, allowing for self-governed mission completion or handling of emergency situations. In this letter, we propose a perception system addressing this challenge by detecting landing sites based on their texture and geometric shape without using any prior knowledge about the environment. The proposed method considers hazards within the landing region such as terrain roughness and slope, surrounding obstacles that obscure the landing approach path, and the local wind field that is estimated by the on-board EKF. The latter enables applicability of the proposed method on small-scale autonomous planes without landing gear. A safe approach path is computed based on the UAV dynamics, expected state estimation and actuator uncertainty, and the on-board computed elevation map. The proposed framework has been successfully tested on photo-realistic synthetic datasets and in challenging real-world environments.

A Load Transportation Nonlinear Control Strategy Using a Tilt-Rotor UAV

This paper proposes a nonlinear control strategy to solve the trajectory tracking problem of a tilt-rotor Unmanned Aerial Vehicle (UAV) when transporting a suspended load. For the present study, the aim of the control system is to track a desired trajectory of the aircraft with load’s swing-free, even in the presence of external disturbances, parametric uncertainties, unmodeled dynamics, and noisy position measurements with lower sampling frequency than the controller. The whole system modeling is obtained through the Euler-Lagrange formulation considering the dynamics of the tilt-rotor UAV coupled to the suspended load. As for the nonlinear control strategy, an inner-loop control is designed based on input-output feedback linearization combined with the dynamic extension approach to stabilize the attitude and altitude of the UAV assuming nonlinearities, while an outer-loop control law is designed for guiding the aircraft with reduced load swing. The linearized dynamics are controlled using linear mixed H2/H∞ controllers with pole placement constraints. The solution is compared to two simpler control systems: the first one considers the load as a disturbance to the system but does not avoid its swing; the second one is a previous academic result with a three-level cascade strategy. Finally, in order to deal with the problem of position estimation in presence of unknown disturbances and noisy measurements with low sampling frequency, a Linear Kalman Filter with Unknown Inputs is designed for estimating both the aircraft’s translational position and translational disturbances. Simulation results are carried out to corroborate the proposed control strategy.

Systematic Modeling of Rotor-Driving Dynamics for Small Unmanned Aerial Vehicles

This paper proposes a systematic modeling approach of rotor-driving dynamics for small unmanned aerial vehicles (UAVs) based on system identification and first principle-based methods. Both steady state response analyses and frequency-domain identifications are conducted for the rotor, and Comprehensive Identification from Frequency Responses (CIFER) software is mainly utilized for the frequency-domain analysis. Moreover, a novel semi-empirical model integrating the rotor and the electrical speed controller is presented and validated. The demonstrated results and model are promising in UAV dynamics and control applications.

Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images

In this study, reducing motion blur in images taken by our unmanned aerial vehicle is investigated. Since shakes of unmanned aerial vehicle cause motion blur in taken images, autonomous performance of our unmanned aerial vehicle is maximized to prevent it from shakes. In order to maximize autonomous performance of unmanned aerial vehicle (i.e. to reduce motion blur), initially, camera mounted unmanned aerial vehicle dynamics are obtained. Then, optimum location of unmanned aerial vehicle camera is estimated by considering unmanned aerial vehicle dynamics and autopilot parameters. After improving unmanned aerial vehicle by optimum camera location, dynamics and controller parameters, it is called as improved autonomous controlled unmanned aerial vehicle. Also, unmanned aerial vehicle with camera fixed at the closest point to center of gravity is called as standard autonomous controlled unmanned aerial vehicle. Both improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles are performed in real time flights, and approximately same trajectories are tracked. In order to compare performance of improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles in reducing motion blur, a motion blur kernel model which is derived using recorded roll, pitch and yaw angles of unmanned aerial vehicle is improved. Finally, taken images are simulated to examine effect of unmanned aerial vehicle shakes. In comparison with standard autonomous controlled flight, important improvements on reducing motion blur are demonstrated by improved autonomous controlled unmanned aerial vehicle.

An Adaptive Dynamic Controller for Quadrotor to Perform Trajectory Tracking Tasks

This work proposes an adaptive dynamic controller to guide an unmanned aerial vehicle (UAV) when accomplishing trajectory tracking tasks. The controller structure consists of a kinematic controller that generates reference commands to a dynamic compensator in charge of changing the reference commands according to the system dynamics. The final control actions thus generated are then sent to the UAV to make it to track an arbitrary trajectory in the 3D space. The parameters of the dynamic compensator are directly updated during navigation, configuring a directly updated self-tuning regulator with input error, aiming at reducing the tracking errors, thus improving the system performance in task accomplishment. After describing the control system thus designed, its stability is proved using the Lyapunov theory. To validate the proposed system simulations and real experiments were run, some of them are reported here, whose results demonstrate the effectiveness of the proposed control system and its good performance, even when the initial values of the parameters associated to the dynamic model of the UAV are completely unknown. One of the conclusions, regarding the results obtained, is that the proposed system can be used as if it were an on-line identification subsystem, since the parameters converge to values that effectively represent the UAV dynamics.

RISE and disturbance compensation based trajectory tracking control for a quadrotor UAV without velocity measurements

This paper presents a velocity-free robust trajectory tracking control for a quadrotor unmanned aerial vehicle (UAV) with consideration of parametric uncertainties and external disturbances by effectively integrating robust integral of the sign of the error (RISE) feedback control with extended state observer (ESO). The original cascaded dynamics of quadrotor UAV is first derived in a strict form with lumped disturbances. Then, the robust RISE partial state feedback controllers with disturbance compensation are respectively synthesized in position and attitude loop, where the unmeasurable velocity states and disturbance compensation terms are estimated by ESO, the synthesized RISE control law is then accounted for attenuating the residual estimation error to achieve enhanced robustness against uncertainties. The major feature of proposed method is that fundamentally different anti-disturbance mechanisms of disturbance suppression-based RISE control and disturbance observer-based control are combined to handle the lumped disturbances simultaneously, which preserves their theoretical advantages while overcoming their performance limitations. Moreover, the proposed controller theoretically guarantees that the tracking error converges to a small neighborhood around the origin. The effectiveness and superiority of proposed control method are investigated in simulations against disturbances due to parametric uncertainties, wind gust and bounded perturbations.

Optimal Rendezvous Trajectory for Unmanned Aerial-Ground Vehicles

Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low-cost aerial surveillance and mapping applications in remote regions. There is, however, a key limitation, which is the fact that low-cost UAVs have limited fuel capacity and, hence, require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in the fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in three-dimensional taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach.

Reconfigurable Fault Tolerant Flight Control for UAV with Pseudo-Inverse Technique

Abstract In this study, the parameter identification based fault tolerant control of an unmanned aerial vehicle (UAV) dynamics is presented. When faults occur in the actuator, then the elements of the control distribution matrix (control derivatives) change. The augmented Kalman filter (KF) is used in order to identify the control distribution matrix elements; thus, the control reconfiguration is carried out using this identified control distribution matrix. A reconfigurable pseudo-inverse controller is designed for the modeled UAV. The control derivatives are identified by the augmented KF and pseudo-inverse controller is reconfigured for the identified control distribution matrix. In simulations, the linearized model of longitudinal dynamics of ZAGI UAV is considered, and the performance of the proposed system identification and reconfigurable control techniques are examined for this model. For this purpose the control derivatives related to elevator and thrust are changed in accordance with faulty system condition and the augmented KF based reconfigurable control algorithm using pseudo-inverse technique is investigated.

Icing diagnosis in unmanned aerial vehicles using an LPV multiple model estimator

The phenomenon of icing, i.e. ice accretion on aircraft surfaces, affects the flight performance of unmanned aerial vehicles (UAVs). Autonomous icing detection schemes are needed in order to assure high efficiency and limit energy consumption of de-icing and anti-icing schemes. The novel contribution of this paper is to apply a linear parameter varying multiple model adaptive estimator to the model of the longitudinal nonlinear dynamics of a UAV, in order to achieve an icing diagnosis that provides information about the icing location. An advantage of applying a linear parameter varying approach is that the icing diagnosis scheme is consistent with the UAV dynamics for a wide range of operating conditions, and it uses only existing standard sensors. Simulation results are used to illustrate the application of the proposed method.

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

ABSTRACTThe Unmanned Aerial Vehicles (UAVs) become more and more popular due to various potential application fields. This paper studies the distributed leader-follower formation flight control problem of multiple UAVs with uncertain parameters for both the leader and followers. This problem has not been addressed in the literature. Most of the existing literature considers the leader-follower formation control strategy with parametric uncertainty for the followers. However, they do not take the leader parametric uncertainty into account. Meanwhile, the distributed control strategy depends on less information interactions and is more likely to avoid information conflict. The dynamic model of the UAVs is established based on the aerodynamic parameters. The establishment of the topology structure between a collection of UAVs is based on the algebraic graph theory. To handle the parametric uncertainty of the UAVs dynamics, a multivariable model reference adaptive control (MRAC) method is addressed to design the control law, which enables follower UAVs to track the leader UAV. The stability of the formation flight control system is proved by the Lyapunov theory. Simulation results show that the proposed distributed adaptive leader-following formation flight control system has stronger robustness and adaptivity than the fixed control system, as well as the existing adaptive control system.

Student research highlights: Quadrotor electronics and intelligent control

Unmanned aerial vehicles (UAVs) have seen diverse applications, including radiation or border surveillance, with quadrotors offering small size, relatively low cost, high mobility, mass production, and easy access to challenging environments. In addition, quadrotors can carry sensors, cameras, or other gadgets for information collection, data fusion, and subsequent actions of targets, as was exemplified during the Fukushima nuclear plant disaster, by visual and sensory surveillance or for an air delivery system. UAVs have also spurred extensive research on physical design, operation principles, components, and control algorithms, partially due to complex stabilization problems, including the Stanford testbed of autonomous rotorcraft for multiagent control [1] quadrotor with linear controllers for position control systems, the X-4 flyer [2] with discrete model controllers, and the MikroKopter [3] with an open-source code development, as well as implementation and control with state estimation. Inherently nonlinear UAV dynamics can also be governed by intelligent controllers that can handle complex, nonlinear systems for superior outcomes. As an intelligent framework, fuzzy logic (FL) reflects real-life vagueness and imprecise data to provide decisions via nonlinear mapping via fuzzy sets, rules, and membership functions [4]. This project describes the development and implementation of a practical, low cost, lightweight quadrotor electronic test platform design to evaluate the FL closed-loop controller for roll and pitch stabilization.

Nonlinear hierarchical control of a quad tilt‐wing UAV: An adaptive control approach

SummaryIn this paper, a nonlinear hierarchical adaptive control framework is proposed for the control of a quad tilt‐wing unmanned aerial vehicle (UAV). An outer loop model reference adaptive controller with robustifying terms creates required forces to be able to move the UAV on a reference trajectory, and an inner loop nonlinear adaptive controller realizes the required attitude angles to achieve these forces. A rigorous stability analysis is provided showing the boundedness of all the signals in this cascaded controller structure. The development and the stability analysis of the controller do not use any linearizations and use the full nonlinear UAV dynamics. The controller is implemented on a high‐fidelity nonlinear tilt‐wing quadrotor model in the presence of uncertainties, wind disturbances, and measurement noise as well as actuator and structural failures. In this work, in addition to earlier modeling studies, the effect of wing‐angle variations, actuator failures, and structural failures and their effect on the center of gravity of the UAV are rigorously and systematically investigated and reflected in the model. Simulation results showing the performance of the proposed controller and a comparison with the fixed controller used in earlier studies are presented in the paper.

Unmanned aerial vehicle trajectory planning by an integrated algorithm in a complex obstacle environment

Both the artificial potential field method and direct method for the optimal control problem have shortcomings in terms of effectiveness and computational complexity for the trajectory-planning problem. This paper proposes an integrated algorithm combining the artificial potential field method and direct method for planning in a complex obstacle-rich environment. More realistic unmanned aerial vehicle dynamics equations, which are rarely applied in the traditional artificial potential field method, are considered in this paper. Furthermore, an additional control force is introduced to transcribe the artificial potential field model into an optimal control problem, and the equality/inequality constraints on the description of the shape of the obstacles are substituted by the repulsive force originating from all the obstacles. The Legendre pseudospectral method and virtual motion camouflage are both utilized to solve the modified optimal control problem for comparison purposes. The algorithm presented in this paper improves the performance of solving the trajectory-planning problem in an obstacle-rich environment. In particular, the algorithm is suitable for addressing some conditions that cannot be considered by the traditional artificial potential field method or direct method individually, such as local extreme value points and a large numbers of constraints. Two simulation examples, a single cube-shaped obstacle and a different-shaped obstacle-rich environment, are solved to demonstrate the capabilities and performance of the proposed algorithm.

A new concept for UAV landing gear shock vibration control using pre-straining spring momentum exchange impact damper

This study proposes a new method for reducing the shock vibration response of an Unmanned Aerial Vehicle (UAV) during the landing process by means of the momentum exchange principle (MEID). The performance of the impact damper is improved by adding a pre-straining spring to the damper system. This research discusses the theoretical application of the damper to the UAV landing gear system. The UAV dynamics is first modeled as a simple lumped mass translational vibration system. Then we analyze a more complex two-dimensional model of UAV dynamics. This model consists of the main wheel, nose wheel and main body. Three cases of UAV landing gear mechanisms: without damper, with passive MEID (PMEID) and with pre-straining spring MEID (PSMEID) are simulated. The damper performance is evaluated from the maximum acceleration and force transmission to the main body. The energy balance calculation is conducted to investigate the performance of PSMEID. The simulation results show that the proposed PSMEID method is the most effective method for reducing the maximum acceleration and force transmission of UAV during impact landing.

Recent Advances in Control Theory and Applications in Turkey

TOK’14, the National Symposium on Automatic Control, is an annually organized event bringing together the researchers from various universities and industry in Turkey. The symposium in 2014 was hosted by the Kocaeli University on 11–13 September in Kocaeli. The current special issue contains eight papers from the symposium programme, which had more than 200 contributions. Control theory is used for strategically cutting-edge high technologies, which provide important support for the sustainable development of flexible and autonomous manufacturing, medicine, automative, space, aerospace, finance, robotics, power and energy systems, defence, networking, etc. The focus of the special issue is to report the recent advances in control theory and applications in Turkey. Recently, national conferences and symposia are becoming hosts for more qualified research studies and industrial projects, as the research and development funds supported by the Turkish government have been rapidly increased. The goal of this special issue is to provide a forum for the latest research and projects, focusing on control theory and their industrial applications. The first paper, contributed by Morgul et al., proposed an alternative approach that steers away from explicit mechanical modelling towards data-driven system identification employed in vertical hopping robots. This identification approach treats the system as a black box and produces empirical models using harmonic transfer functions (HTF). Using this method reduces the complexity of the mathematical models and approximates unmodelled dynamics in legged locomotion robots. They showed that HTFs can be used as predictors of simple locomotion models on training data and their performance on sinusoid inputs is much better than on step inputs. The second paper, by Ozdogan and Leblebicioglu, presented modelling of a single axis (inner azimuth gimbal) of a four-axis gyro-stabilized electro-optic gimbal system using frequency response function (FRF). FRF is existing technique in the literature but its application to an electro-optic gimbal system is a challenging problem. The third paper, contributed by Efe et al., compared the performance of the controllers, namely proportional–integral–derivative control, sliding mode control, backstepping control, feedback linearization-based control and fuzzy control, based on integral absolute error (IAE), maximum absolute error (MAE), integral squared error (ISE), integral time squared error (ITSE) and error variance (EV) for Quadrotor-type aerial vehicles. They found that sliding mode control is better than the others in terms of simplicity and closed-loop control performance. In the fourth paper, Iplikci and Bahtiyar developed the Runge–Kutta model predictive controller (RKMPC) implemented in real time on a field-programmable gate array (FPGA) in an electromagnetic levitation system. They compared performance of the RKMPC with a conventional nonlinear model predictive controller (NMPC) method for step and sinusoidal inputs. They found that the RKMPC provides very satisfactory control performance for a very fast experimental non-linear system. The fifth paper, authored by Alagoz and Kaygusuz, examined smart grid energy market management performance of a fractional-order proportional–integral (PI) controller in the case of communication and operation delays in a multisource energy market model. The simulation results showed that using of the fractional-order PI controller decreases the average energy shortage error and price volatility in the smart grid energy markets. Hence, it can be a good candidate for automated market management applications. In the sixth paper, Bagis and Konar studied a performance comparison of the Sugeno and Mamdani-type fuzzy models optimized by the artificial bee colony (ABC) algorithm with the other modelling approaches in the literature for a microstrip antenna. The ABC algorithm is found to be a powerful candidate for solving the numerical optimization of fuzzy models. In the seventh paper, Leblebicioglu et al. solved the path planning problem using a genetic algorithm (GA) with the proposal of novel evolutionary operators for multiple unmanned aerial vehicles (UAVs). They obtained nearly global optimum flight path solutions regarding UAV dynamics. Real-world experiments conducted using small UAVs with

Dubins path‐based dynamic soaring trajectory planning and tracking control in a gradient wind field

SummaryThis paper studies small unmanned aerial vehicle dynamic soaring for conserving onboard energy to extend flight endurance performance. A novel dynamic soaring path planning approach is proposed using the Dubins path. It enjoys a significant improvement in computational efficiency. In addition, a custom‐built trajectory tracking controller is developed for a nonlinear unmanned aerial vehicle dynamics model to verify the implementation of the proposed path planning approach. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed design and development. Copyright © 2016 John Wiley & Sons, Ltd.