Nonlinear Unmanned Aerial Vehicle Research Articles

A Decomposition and a Scheduling Framework for Enabling Aerial 3D Printing

Aerial 3D printing is a pioneering technology yet in its conceptual stage that combines frontiers of 3D printing and Unmanned aerial vehicles (UAVs) aiming to construct large-scale structures in remote and hard-to-reach locations autonomously. The envisioned technology will enable a paradigm shift in the construction and manufacturing industries by utilizing UAVs as precision flying construction workers. However, the limited payload-carrying capacity of the UAVs, along with the intricate dexterity required for manipulation and planning, imposes a formidable barrier to overcome. Aiming to surpass these issues, a novel aerial decomposition-based and scheduling 3D printing framework is presented in this article, which considers a near-optimal decomposition of the original 3D shape of the model into smaller, more manageable sub-parts called chunks. This is achieved by searching for planar cuts based on a heuristic function incorporating necessary constraints associated with the interconnectivity between subparts, while avoiding any possibility of collision between the UAV’s extruder and generated chunks. Additionally, an autonomous task allocation framework is presented, which determines a priority-based sequence to assign each printable chunk to a UAV for manufacturing. The efficacy of the proposed framework is demonstrated using the physics-based Gazebo simulation engine, where various primitive CAD-based aerial 3D constructions are established, accounting for the nonlinear UAVs dynamics, associated motion planning and reactive navigation through Model predictive control.

Robust Leaderless Time-Varying Formation Control for Nonlinear Unmanned Aerial Vehicle Swarm System With Communication Delays.

This article investigates the tracking-oriented robust leaderless time-varying formation (TVF) control problem for unmanned aerial vehicle swarm systems (UAVSSs) with Lipschitz nonlinear dynamics under directed topology, where external disturbances are random and bounded, and communication delays (CDs) are bounded. In this article, a state-feedback control approach is adopted to make sure that a UAVSS forms a desired TVF and follows a specified trajectory when CDs and external disturbances occur. First, a novel PD-like formation control protocol with several unknown parameters and CDs is designed. The protocol contains the information of the local neighborhood status and its differential quantities. Second, the tracking-oriented robust leaderless TVF control problem with Lipschitz dynamics, external disturbances, and CDs is transformed into a problem about asymptotic stability of a lower dimensional closed-loop control system through a special matrix decomposition. Third, a theorem is proposed to determine the unknown parameters of the control protocol and the upper bound of CDs. In the theorem, sufficient conditions for a UAVSS to attain the anticipated TVF and trajectory tracking are obtained. A Lyapunov-Krasovskii (LK) functional is constructed to verify that the error among the practical flight state of UAVs, the anticipant TVF configuration, and tracking trajectory can asymptotically converge to 0. Finally, with the presentation of a simulation case, the effectiveness of the theoretical results is illustrated.

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.

3D Smooth Trajectory Planning for UAVs under Navigation Relayed by Multiple Stations Using Bézier Curves

Navigation relayed by multiple stations (NRMS) is a promising technique that can significantly extend the operational range of unmanned aerial vehicles (UAVs) and hence facilitate the execution of long-range tasks. However, NRMS employs multiple external stations in sequence to guide a UAV to its destination, introducing additional variables and constraints for UAV trajectory planning. This paper investigates the trajectory planning problem for a UAV under NRMS from its initial location to a pre-determined destination while maintaining a connection with one of the stations for safety reasons. Instead of line segments used in prior studies, a piecewise Bézier curve is applied to represent a smooth trajectory in three-dimensional (3D) continuous space, which brings both benefits and complexity. This problem is a bi-level optimisation problem consisting of upper-level station routing and lower-level UAV trajectory planning. A station sequence must be obtained first to construct a flight corridor for UAV trajectory planning while the planned trajectory evaluates it. To tackle this challenging bi-level optimisation problem, a novel efficient decoupling framework is proposed. First, the upper-level sub-problem is solved by leveraging techniques from graph theory to obtain an approximate station sequence. Then, an alternative minimisation-based algorithm is presented to address the non-linear and non-convex UAV trajectory planning sub-problem by optimising the spatial and temporal parameters of the piecewise Bézier curve iteratively. Computational experiments demonstrate the efficiency of the proposed decoupling framework and the quality of the obtained approximate station sequence. Additionally, the alternative minimisation-based algorithm is shown to outperform other non-linear optimisation methods in finding a better trajectory for the UAV within the given computational time.

A Comparative Study for Control of Quadrotor UAVs

Modeling and controlling highly nonlinear, multivariable, unstable, coupled and underactuated systems are challenging problems to which a unique solution does not exist. Modeling and control of Unmanned Aerial Vehicles (UAVs) with four rotors fall into that category of problems. In this paper, a nonlinear quadrotor UAV dynamical model is developed with the Newton–Euler method, and a control architecture is proposed for 3D trajectory tracking. The controller design is decoupled into two parts: an inner loop for attitude stabilization and an outer loop for trajectory tracking. A few attitude stabilization methods are discussed, implemented and compared, considering the following control approaches: Proportional–Integral–Derivative (PID), Linear–Quadratic Regulator (LQR), Model Predictive Control (MPC), Feedback Linearization (FL) and Sliding Mode Control (SMC). This paper is intended to serve as a guideline work for selecting quadcopters’ control strategies, both in terms of quantitative and qualitative considerations. PID and LQR controllers are designed, exploiting the model linearized about the hovering condition, while MPC, FL and SMC directly exploit the nonlinear model, with minor simplifications. The fast dynamics ensured by the SMC-based controller together with its robustness and the limited estimated command effort of the controller make it the most promising controller for quadrotor attitude stabilization. The outer loop consists of three independent PID controllers: one for altitude control and the other two, together with a dynamics’ inversion, are entitled to the computation of the reference attitude for the inner loop. The capability of the controlled closed-loop system of executing complex trajectories is demonstrated by means of simulations in MATLAB/Simulink®.

Robust Backstepping Control Combined with Fractional-Order Tracking Differentiator and Fractional-Order Nonlinear Disturbance Observer for Unknown Quadrotor UAV Systems

In this paper, we studied a fractional-order robust backstepping control (BSC) combined with a fractional-order tracking differentiator and a fractional-order nonlinear disturbance observer for a quadrotor unmanned aerial vehicle (UAV) system. A fractional-order filtered error and a fractional-order tracking differentiator were utilized in a conventional BSC system to improve the positioning control performance of a highly coupled nonlinear quadrotor UAV system and bypass the differentiation issue of the virtual control and compensation of the transformation error in the conventional BSC design. A new fractional-order disturbance observer with the sine hyperbolic function was then proposed to enhance the estimation performance of the uncertain quadrotor UAV. Sequential comparative simulations were conducted, demonstrating that the proposed positioning controller and observer utilizing fractional-order calculus outperformed those of the conventional controller and observer systems.

Physical Modeling, Simulation and Validation of Small Fixed-Wing UAV

The flight dynamics of small Unmanned Aerial Vehicles (UAVs) exhibits substantial nonlinear features which should not be ignored in simulation or analysis. In this work, a complete six degrees of freedom (6DOF) and high-fidelity simulation model of a case study of a small fixed-wing UAV is developed. To accurately estimate the nonlinear UAV’s mathematical model, the mass-inertia properties are investigated through experiments. Moreover, the aerodynamic model characteristics are estimated using a semi-empirical technique to estimate the aerodynamic coefficients and derivatives. The propulsion system’s physical characteristics are estimated and analyzed through experimental measurements. The International Standard Atmosphere (ISA) tables are used to develop the atmospheric model. Furthermore, the actuation model is estimated and validated experimentally through system identification techniques. These estimated sub-model data are integrated to complete the nonlinear flight dynamics model in MATLAB® (Simulink). The UAV trim calculations are graphically derived and then the developed model is tested and verified. The open loop simulation results have proved the superiority of the utilized model techniques to describe the UAV motion. Moreover, it gives very optimistic results to test the flight dynamics of the UAV and design a consistent flight control and guidance system.

Control allocation technology based on fault diagnosis for the unmanned aerial vehicle system subject to physical constraints and fault reconfiguration mismatch

AbstractThe problem of the system robustness subject to physical constraints and mismatched fault reconstruction is discussed in this paper. In order to facilitate the design, a four‐rotor unmanned aerial vehicle (UAV) system model was selected for research. First, the control allocation model of the nonlinear UAV system with disturbances is shown in the paper. Secondly, a weighted pseudo‐inverse method based on adaptive weights is proposed, which reduces the impact of physical constraints on the system. After that, a dynamic weight control allocation method based on the fault efficiency matrix is designed. The weight matrix can dynamically adjust the control distribution law according to the fault estimation value provided by the observer. Then, a dynamic adaptive control allocation method for faults and physical constraints is carried out by combining adaptive weights and dynamic weights. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper.

A Distributed Control for Ground Target Tracking of Nonlinear Unmanned Aerial Vehicles Formation

This paper investigates a circular formation control strategy for multiple unmanned aerial vehicles (multi-UAV) formation for ground moving target tracking. Each UAV obtains the observation value of target information through sensors and exchanges data with its neighbors. Since the formation cannot get the parameter of the target directly, we proposed a distributed error observer. A new guidance law based on the error value obtained has been designed, and it was proved through the interconnection system that it was consistent with Lyapunov stability. Finally, a simulation example is used to illustrate the effectiveness of the proposed guidance law.

Robust Super-Twisting Sliding Mode Backstepping Control Blended with Tracking Differentiator and Nonlinear Disturbance Observer for an Unknown UAV System

In this study, we examined the robust super-twisting sliding mode backstepping control (SBSC) method, which employed a tracking differentiator and nonlinear disturbance observer for a hexacopter unmanned aerial vehicle (UAV) system. To realize robust tracking control performance for a highly coupled nonlinear hexacopter UAV system, a super-twisting sliding mode control method was combined with designing stabilizing controls of backstepping control (BSC) applied to the UAV system. Furthermore, the differentiation issue of the virtual control and compensation of transformation error in the conventional BSC design were bypassed via a new continuous tracking differentiator structure. Additionally, a new disturbance observer based on the proposed tracking differentiator was considered to estimate uncertainties of the hexacopter UAV. Comparative simulation results demonstrated that the proposed tracking-differentiator-based SBSC scheme (PTSBSC) blended with the tracking differentiator and nonlinear disturbance observer exhibits improved performance when compared to that of conventional BSC and disturbance observer systems.

A stability perspective of bioinspired unmanned aerial vehicles performing optimal dynamic soaring

The phenomenon of dynamic soaring, as exhibited by soaring birds, has long been a biological inspiration for aerospace and control engineers. If this fascinating phenomenon, which allows soaring birds to perform almost unpowered flight using wind shear, can be mimicked by unmanned aerial vehicles (UAVs), then there is substantial potential for technological and economic enhancement of UAV performance. Although there has been a considerable amount of research covering the modeling, optimization, control and simulation aspects of different UAVs performing dynamic soaring, there is little to no conclusive work analyzing the stability of such UAVs in soaring orbits. In this paper we present a comprehensive framework for determining the stability of soaring UAVs utilizing both linear (Floquet-based) and nonlinear (contraction theory-based) techniques. Floquet stability analysis was inconclusive, which led to the use of a nonlinear contraction formulation to reach a conclusive stability assessment for an actual nonlinear fixed-wing UAV performing dynamic soaring. Furthermore, parametric variation along with numerical simulations were conducted to ascertain the response of the actual nonlinear system when perturbed from the nominal motion studied in this paper. The analysis and simulations revealed that the system possesses instability as the UAV motion diverges from its nominal trajectory and follows an undesirable path. From this result we conclude, for the first time in the literature as far as we are aware, that UAVs performing dynamic soaring in an optimal way to reduce wind shear requirements are inherently unstable. The results of this work suggest that mimicking of dynamic soaring by UAVs will require careful investigation of tracking and regulatory controls that need to be implemented.

Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC

Purpose This paper aims to propose a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more accurately. Thus, the controls should ensure that the UAV swarm follows a moving circular path whose center is the target while also attaining and maintaining a circular formation of a specific geometric shape; and the formation control system is capable of self-tuning because the UAV dynamics is uncertain. Design/methodology/approach This paper considers two interaction architectures: an open-chain where each UAV only interacts with its neighbors; and a cooperative leader, where the leading UAV is involved in attaining the formation. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC). Findings Using open-chain decentralized architecture allows to have an unlimited number of aircraft in a formation, which is in line with the swarm behavior concept. The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models. Research limitations/implications Assume the target follows a rectilinear trajectory at a constant speed. The speed is supposed to be known in advance. Another assumption is that the weather is windless. Originality/value In contrast to known studies, this one uses Lyapunov guidance vector fields that are direction- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus.

Robust fault diagnosis and fault-tolerant control for nonlinear quadrotor unmanned aerial vehicle system with unknown actuator faults

This article addresses the problem that quadrotor unmanned aerial vehicle (UAV) actuator faults, including small-amplitude bias faults and gain degradation, cannot be detected in time. A hybrid observer, which combines the fast convergence from adaptive observer and the strong robustness from sliding mode observer, is proposed to detect and estimate UAV actuator faults accurately with model uncertainties and disturbances. A nonlinear quadrotor UAV model with model uncertainties and disturbances is considered and a more precise unified expression for actuator faults that do not require knowing where the upper or lower bound is provided. The original system is decomposed into two subsystems by coordinate transformation to improve detection accuracy for small amplitude bias faults and avoid external influences. The hybrid observer is then designed to estimate subsystem states and faults with good stability by selecting a Lyapunov function. A fault-tolerant controller is obtained depending on fault estimation by compensating the normal controller (proportion integral differential [PID] controller). Several numerical simulations confirmed that unknown actuator faults can be accurately detected, estimated, and compensated for even under disturbance conditions.

Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping

The paper proposes a novel approach to control fixed-wing unmanned aerial vehicle (UAV) swarm to start and keep flying in a parallel formation of a specific geometry. The distinctive feature of this method is that it is infinitely scalable thanks to synthesizing decentralized cooperative control laws that are globally asymptotically stable. These laws use the architecture of leaderless consensus, in which each UAV communicates with the adjacent UAVs only. Unlike standard linear consensus, these formation controllers consider both the nonholonomic dynamics of fixed-wing UAV and the input constraints for the autopilot-UAV system, which enables the aircraft to take any initial position before the algorithms start running. Together, coordinated control laws generate vector field for cooperative rectilinear path following, a field that is non-uniform in direction and magnitude. The research further applies backstepping techniques to generate control inputs for a more realistic autopilot-UAV model, which is adjusted in view of the fact that the autopilot response to speed- and path-related input is naturally first-order. We have implemented adaptive parametric self-tuning in autopilot-UAV models, as flight can affect the dynamics of any UAV. As a result, each UAV takes its own spot in a parallel formation, whereby each aircraft's speed asymptotically tends to the final cruising speed of the formation while the course angle tends to final course angle, which helps keep the formation stable and its geometry precise. The effectiveness of the proposed approach has been tested in MATLAB/Simulink using six-DOF 12-state non-linear fixed-wing UAV models.

Autonomous Navigation of a Solar-Powered UAV for Secure Communication in Urban Environments with Eavesdropping Avoidance

This paper considers the navigation of a solar-powered unmanned aerial vehicle (UAV) for securing the communication with an intended ground node in the presence of eavesdroppers in urban environments. To complete this task, the UAV needs to not only fly safely in the complex urban environment, but also take into account the communication performance with the intended node and eavesdroppers. To this end, we formulate a multi-objective optimization problem to plan the UAV path. This problem jointly considers the maximization of the residual energy of the solar-powered UAV at the end of the mission, the maximization of the time period in which the UAV can securely communicate with the intended node and the minimization of the time to reach the destination. We pay attention to the impact of the buildings in the urban environments, which may block the transmitted signals and also create some shadow region where the UAV cannot harvest energy. A Rapidly-exploring Random Tree (RRT) based path planning scheme is presented. This scheme captures the nonlinear UAV motion model, and is computationally efficient considering the randomness nature. From the generated tree, a set of possible paths can be found. We evaluate the security of the wireless communication, compute the overall energy consumption as well as the harvested amount for each path and calculate the time to complete the flight. Compared to a general RRT scheme, the proposed method enables a large time window for the UAV to securely transmit data.

Adaptive decentralized flocking control of multi-UAV circular formations based on vector fields and backstepping

This is a paper on controlling fixed-wing unmanned aerial vehicle (UAV) swarm formations while coordinating their flocking to a specified circular path. The proposed non-uniform in both magnitude and direction path-following vector fields enable the aircraft of the entire group to converge to a circular motion around a target while also attaining and maintaining relative phase-shift angles between the UAVs. It is thereby assumed that UAVs use decentralized consensus for their neighbor–neighbor​ coordination, which implies unconstrained scalability of the formation. The highlight of this research is that it gets rid of the conventional assumption that all the UAVs must initially be on a circular path and follow it strictly, which makes the proposed approach more practical. The obtained backstepping-based control commands explicitly factor in the input constraints and make the UAV course angles and speeds converge to the vector field-specified values. The inevitable parameter uncertainties of UAV kinematic models can destabilize the formation, which is why adaptive self-tuning is applied. The new decentralized UAV flocking controller has been tested by detailed numerical MATLAB/Simulink experiments, including comparative experimentation, using realistic six degree-of-freedom (DoF) 12-state nonlinear UAV models; numerical modeling demonstrates the proposed approach stable for a variety of initial conditions.

Development of methods to model UAVS nonlinear automatic control systems

When modeling Automatic Control Systems (ACS) of an unmanned aerial vehicle (UAV), it is often necessary to take into account the nonlinearity of an aircraft's reaction when the controls drift, as well as the strong influence of various destabilizing factors that make the system go out of linear mode. When known analytical and numerical methods are used to analyze dynamic systems, it is problematic to obtain general solutions that are valid for the variable parameters of the system under study and, at the same time, provide the required error value. A method has been developed to model dynamic processes in automatic non-linear UAV control systems based on linear approximation by parts and crosslinking of partial solutions with consideration of the initial conditions. An example of using the technique to model the transition characteristics of an ACS UAV with a single non-linear link is considered. Based on the analysis of errors in the calculation of the transition process, the effectiveness of the proposed approach is shown.

Nonlinear unmanned aerial vehicle formation control via composite Laplacian quadratics

This article studies a leader‐following formation control problem, in which the agent model is governed by continuous‐time nonlinear differential equations. We are interested in designing a distributed control algorithm which is capable of handling the strong nonlinearities induced by the unmanned aerial vehicle model. Our primary objective is to investigate the convergence of the corresponding closed‐loop system, and to understand how the nonlinear dynamics and the network topologies may impose intrinsic barriers on formation control. The results show that an exponential convergence is guaranteed under the proposed algorithm via the composite Laplacian quadratics function as long as the network topology has a directed spanning tree.

An Observer and Post Filter Based Scheme for Fault Estimation of Nonlinear Systems

The problem of actuator fault estimation for a kind of nonlinear discrete time varying (NDTV) systems is investigated. Norm bounded disturbance is considered and the nonlinear function is assumed to fulfill a Lipschitzlike condition. Fault estimation is accomplished by a novel nonlinear state observer and a dynamic post filter. First, the nonlinear state observer is designed with the help of the small gain theorem and the H∞ filtering approach. Then for the error dynamic of the observer, the dynamic post filter is constructed to estimate the fault. In this scheme, no approximation of the nonlinear function is taken, and the problem of infeasibility resulting from multiple synthesis conditions is considerably improved. Simulation studies are carried out with a nonlinear unmanned aerial vehicle (UAV) model. The turbulent condition is considered and the feed back control loop is employed. Simulation results show that the proposed method can accomplish the estimation work, while the residual evaluation based approach fails to detect the fault.

Nonlinear Adaptive Line-of-Sight Path Following Control of Unmanned Aerial Vehicles considering Sideslip Amendment and System Constraints

With growing worldwide interests in commercial, scientific, and military issues, there has been a corresponding rapid growth in demand for the development of unmanned aerial vehicles (UAVs) with more reliable and safer motion control abilities. This paper presents a new nonlinear path following scheme integrated with a heading control law for achieving accurate and reliable path following performance. Both backstepping and finite-time techniques are employed for developing the path following and heading control strategies capable of minimizing cross-track errors in finite-time with elegant transient performance, while the barrier Lyapunov function scheme is adopted to limit turning rates of the UAV for preventing it from capsizing which may be induced by overquick steering actions. A fixed-time nonlinear estimator, based on UAV kinematics, is designed for estimating the uncertainties with sideslip angles caused by external disturbances and inertial motions. To avoid the complicated calculation of derivatives of virtual control terms in backstepping, command filters and auxiliary systems are likewise introduced in the design of control laws. Extensive numerical simulation studies on a nonlinear UAV model are conducted to demonstrate the effectiveness of the proposed methodologies.