Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Refined fault tolerant tracking control of fixed-wing UAVs via fractional calculus and interval type-2 fuzzy neural network under event-triggered communication

SummaryAn adaptive fast nonsingular integral terminal sliding mode control scheme is proposed for the distributed formation control problem of multiple unmanned aerial vehicles (UAVs) affected by actuator faults and external disturbances. In order to achieve a good cooperative tracking performance of UAVs during mission execution, the adverse effect of the lumped disturbances composed of actuator failures, wind and vortex disturbances is estimated by the cerebellar model articulation neural network, meanwhile the tangent function is introduced to eliminate the chattering of the sliding mode. Then the analysis of superior convergence performance of the fast nonsingular integral terminal sliding manifold is implemented, and the finite time stability of the closed‐loop formation flight control system is proved. Finally, the simulation experiments verify that the proposed approach is superior to the nonsingular integral terminal sliding mode control for the formation control system of faulty multiple UAVs.

: Unmanned aerial vehicles (UAVs) are critical components of the future naval forces. UAV control and monitoring with autonomous operation will become an absolute necessity and adaptive cooperation of vehicles is the only practical alternative. The objective of this project is to develop and evaluate new methodologies for cooperative (formation) control of multiple unmanned air vehicles. The goal is to have multiple UAVs working together as a group. Instead of separately assigning distinct tasks to each vehicle, the operator would assign tasks to the UAV group, which then determines the best way to accomplish each task, freeing the operator to maintain surveillance over the entire operation. In this project we investigated Path Tracking and obstacle avoidance of UAVs using fuzzy logic method. Algorithms for close formation control of multi-UAVs are developed and simulated. We also investigated fault-tolerant control of single UAVs by neuro-adaptive method. Detailed description of this method is provided in this document. The project has supported S graduate students with 9 technical papers published.

Active fault-tolerant control of quadrotor UAVs with nonlinear observer-based sliding mode control validated through hardware in the loop experiments

In this article, the adaptive event-triggered fault-tolerant control (FTC) issue of uncertain nonlinear systems suffering from sensor and actuator faults as well as external disturbances is studied. A disturbance observer (DO) is constructed to compensate for external disturbances and approximation errors generated by neural networks (NNs). Then, switching event-triggered control and command filtering techniques are introduced to balance the communication frequency and tracking performance of the controlled systems while avoiding “explosion of complexity” issue caused by iterative derivations of virtual controllers. Furthermore, compensation signals are designed to eliminate filtered errors. Finally, it is proven that the developed FTC scheme can esure that all signals are bounded and free from Zeno phenomenon. Simulation results of a spring damping mechanical system verifies the merits of the designed control algorithm.

Event-triggered adaptive fixed-time fuzzy control for uncertain nonlinear systems with unknown actuator faults

The unmanned aerial vehicle (UAV) has been developing rapidly recently, and the safety and the reliability of the UAV are significant to the mission execution and the life of UAV. Sensor and actuator failures of a UAV are one of the most common malfunctions, threating the safety and life of the UAV. Fault-tolerant control technology is an effective method to improve the reliability and safety of UAV, which also contributes to vehicle health management (VHM). This paper deals with the sliding mode fault-tolerant control of the UAV, considering the failures of sensor and actuator. Firstly, a terminal sliding surface is designed to ensure the state of the system on the sliding mode surface throughout the control process based on the simplified coupling dynamic model. Then, the sliding mode control (SMC) method combined with the RBF neural network algorithm is used to design the parameters of the sliding mode controller, and with this, the efficiency of the design process is improved and system chattering is minimized. Finally, the Simulink simulations are carried out using a fault tolerance controller under the conditions where accelerometer sensor, gyroscope sensor or actuator failures is assumed. The results show that the proposed control strategy is quite an effective method for the control of UAVs with accelerometer sensor, gyroscope sensor or actuator failures.

This paper presents a robust adaptive fault-tolerant control (FTC) strategy for the path-following maneuvering of a high-speed unmanned aerial vehicle (UAV) formation system. The designation integrates an actuator gain-adaptive mechanism which is capable of compensating partial loss of effectiveness and bias faults, with a cascaded event-triggered mechanism (ETM) that regulates both control-command updates and adaptation loops. To handle strong coupling and modeling uncertainties in the UAV dynamics, unknown nonlinear terms are approximated using a fuzzy logic system (FLS), and dynamic surface control (DSC) is employed to avoid differential explosion. A boundary-regulated intermediate control term further enhances robustness against time-varying gains. The cascaded ETM reduces communication and computation by enforcing update thresholds on control inputs and parameter-update signals. Lyapunov analysis establishes semi-global uniform ultimate boundedness of all closed-loop signals. Comparative simulations indicate improved tracking accuracy and reduced channel load relative to representative baselines.

Dynamic event-triggered composite anti-disturbance fault-tolerant tracking control for ships with disturbances and actuator faults

In this paper, an augmented sliding mode-based fault-tolerant control is designed theoretically, implemented practically and tested experimentally in a quadrotor unmanned aerial vehicle testbed under propeller damage and actuator fault conditions for tracking control. In view of the significant feature of robustness inherent to the sliding mode control technique, the developed sliding mode-based fault-tolerant control strategies have been designed and implemented in the two currently and widely used types of fault-tolerant control strategy, i.e. passive and active, with the intention to investigate and compare the advantages, disadvantages and application considerations and limitations of these two different fault-tolerant control strategies in the tracking control problem of a quadrotor unmanned aerial vehicle application. Therefore, these two types of controller have been carried out in both theory and practice with and without the presence of faults. Both theoretical and experimental analyses demonstrated the effectiveness of the two sliding mode-based fault-tolerant control strategies in the application to the quadrotor unmanned aerial vehicle under a small level of actuator faults or damage. Detailed comparisons are also provided in the paper to demonstrate the capabilities, advantages and disadvantages of the two types of fault-tolerant tracking controller under different flight conditions in the presence of actuator faults and propeller damage.

To archive diverse kinds of goal, multiple unmanned aerial vehicles (UAVs) need to cooperate each other. Therefore, the importance of a ground station becomes more important. This paper proposes a framework of the ground station that controls multiple and hetero UAVs. The framework contains multiple UAV control modules and UI interfaces and has the flexibility for adding diverse kinds of UAVs and embedded devices such as commercial UAVs, Arduino-based UAVs, cameras and storage devices.

Unmanned aerial vehicles (UAV) are useful for many applications where human intervention is considered difficult or dangerous. Traditionally, the fixed-wing UAV has been served as the unit for these dangerous tasks because the control is easy. Rotorcraft UAV (RUAV), on the other hand, can operate in many different flight modes which the fixed-wing one is unable to achieve, such as vertical take-off/landing, hovering, lateral flight, pirouette, and bank-to-turn. Due to the versatility in maneuverability, helicopters are capable to fly in and out of restricted areas and hover efficiently for long periods of time. These characteristics make RUAV applicable for many military and civil applications. However, the control of RUAV is difficult. Although some control algorithms have been proposed (Sanders et al., 1998, Garratt et al., 2003, Enns et al., 2000, Bijnens et al., 2005, Koo et al., 1998, Jiang et al., 2006), most of them were verified by simulation instead of real experiments. One reason for this is due to the complicate, nonlinear and inherently unstable dynamics, which has cross coupling between main and tail rotor, and lots of time-varying aerodynamic parameters. Another reason is that the flight test is in high risk. If a RUAV lost its control, it would never be stabilized. Shenyang Institute of Automation, Chinese Academy of Sciences (SIA, CAS) as a national research institute focus its future research on RUAV 5 years ago. Until now, we have 3 types of experimental platforms for advanced control algorithm research demonstration. ServoHeli-20 (Fig.1) is a model class platform which has 20 kilograms takeoff weight. ServoHeli-40 (Fig.2) and ServoHeli-110 (Fig.3) are engineering class platforms for highway patrol, electrical line patrol and photography. They have 40 and 110 kilograms takeoff weight and have finished full autonomous flight control experimental demonstration. In SIA, the control algorithm research on RUAV involves in navigation, advanced flight control, 3D path planning and fault tolerant control. This paper details the development of an unmanned helicopter testbed – ServoHeli-20 (Qi et al., 2006) (Fig.3), and the experiments performed toward achieving full autonomous flight. The brief of this paper is as follow: the ServoHeli-20 platform is introduced in Section II. The introduction of sensor package is in Section III. The modeling of the RUAV system is presented in the Section IV. In Section V, we introduce an independent-channel control scheme as a baseline control of the platform. In Section VI, an overview of fault tolerant

This paper mainly studies the leader follower formation control problem of multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) under time-varying actuator faults. A hierarchical control strategy is proposed for the underactuated characteristics of VTOL UAVs. Based on Lyapunov stability theory, an adaptive fault-tolerant control scheme for the outer position loop was established to achieve tracking of the target position. A fault-tolerant control strategy for adaptive inner attitude loop was designed using Nussbaum function, achieving tracking of instruction attitude. On the premise of ensuring that the inner loop can quickly track the outer loop, the stability of the entire closed-loop system has been successfully achieved. The simulation results validated the proposed theoretical results.

<div>In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.</div><div><br></div><div>In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.</div><div><br></div><div>Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.</div>

Introduction Autonomous unmanned aerial vehicles (UAVs) are considered with increasing interest in a diverse set of civil and commercial applications, such as environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [208, 132, 362, 265, 434, 110, 280, 163]. Use of single or multiple UAVs as communication relays or aerial base stations for network provisioning in emergency situations and for public safety communications has been of particular interest due to UAVs fast deployment and large coverage capabilities [331, 193, 206, 429]. The deployed aerial platform size and the number of necessary platforms are key factors in determining the on-board communication system in addition to the coverage and capacity requirements [73]. Small-scale multi-rotors are of particular interest in practice due to their ease of deployment, low acquisition and maintenance costs, high maneuverability, and ability to hover. Research and development of small-scale UAVs focused initially on control issues, such as flight stability, maneuverability, and robustness, followed by designing autonomous vehicles capable of waypoint flights with minimal user intervention. Considering the limited flight time and payload capacity of these vehicles and the area sizes the aforementioned applications might span, the interest is shifting more and more toward collaborative multiple UAV systems for efficient and successful missions. The number of UAVs might be from tens to hundreds traveling over tens of meters to kilometers for different applications (see Figure 3.1 for an illustration). Wireless communication is an essential factor not only for network provisioning with UAVs but also for successful deployment of systems consisting of multiple small-scale UAVs. It is highly likely that high-performance links and connectivity in three-dimensional space will be required for several applications (e.g., monitoring certain areas) with data delivery meeting a certain quality of service demands [35].The question arises as to which wireless technology should be employed for aerial networks that consist of air-to-ground and air-to-air links, and data needs to be delivered regardless of significant height and orientation differences. Taking into account the quality of service to be satisfied over diverse links and the high node mobility, it is not yet clear as to whether networking protocols developed for ground networks are readily deployable on UAV networks.