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This article considers the decentralized fractional-order fault-tolerant control problem for unmanned aerial vehicles (UAVs) against wind disturbances and actuator faults in a directed communication network. A new composite adaptive disturbance observer-based decentralized fractional-order fault-tolerant control (CADOB-DFO-FTC) scheme, which incorporates fractional-order (FO) sliding-mode surfaces, nonlinear disturbance observers (NDOs), fuzzy wavelet neural networks (FWNNs), and robust controllers, is developed to achieve the attitude tracking control of networked UAVs in a decentralized way. Based on the FO sliding-mode surfaces, the NDOs are first developed to estimate the lumped uncertainties due to the aerodynamic parameter perturbations, wind disturbances, and actuator faults. Then, adaptive FWNNs with updating weighting matrices, mean vectors, and deviation vectors are constructed to effectively attenuate the adverse effects induced by the NDO estimation errors. Furthermore, to compensate the FWNN approximation errors, robust controllers are integrated into the developed control scheme to enhance the approximation abilities. It is shown that by using Lyapunov methods, all UAVs can track their attitude references. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the fault‐tolerant containment control (FTCC) problem for a group of fixed‐wing unmanned aerial vehicles with simultaneous considerations of faults and collision avoidance. A fractional‐order (FO) FTCC scheme is established to steer all follower UAVs into the convex hull formed by the leader UAVs with the involvements of FO calculus, disturbance observers (DOs), and interval type‐2 fuzzy neural networks (IT2FNNs). In the proposed control protocol, FO sliding‐mode surfaces with artificial potential functions are first designed to revamp the filtered containment errors. Then, the DOs with FO calculus are constructed to estimate the FO lumped disturbances due to faults and external disturbances. Moreover, to compensate for the DO estimation errors, IT2FNN learning mechanisms are introduced to improve the FTCC capability. It is shown by Lyapunov stability analysis that all follower UAVs can successfully converge into the convex hull spanned by the leader UAVs without collisions even when a portion of UAVs is encountered by faults. Simulation results are presented to show the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

In a dynamic environment, moving to the destination safely and effectively is of paramount importance for an unmanned ground vehicle (UGV). This article presents a strategy of trajectory planning and tracking that aims to ensure the UGV’s safety in an uncertain environment. Specifically, based on the initial environment information, a global optimal trajectory connecting the start and the destination is predefined by an artificial fish swarm algorithm (AFSA). In the presence of unforeseen obstacles, a trial-based forward search (TFS) algorithm based on the Markov chain is proposed in the local trajectory planning module, while collision prediction is integrated as heuristic information. The vehicle’s current state is updated accordingly for the sake of avoiding entire state spaces involved in the computation. Therefore, the storage efficiency and convergence rate in local path planning are sufficiently enhanced in comparison to dynamic programming. Moreover, command signals can be calculated with the proposed multiconstrained model predictive controller (MMPC), ensuring the vehicle to track the reference trajectory and smoothen the motion. Finally, the results in both simulations and experiments reveal the effectiveness of the proposed algorithm in the presence of both static and dynamic obstacles. Note to Practitioners—This article is motivated by the unmanned ground vehicle (UGV) collision avoidance problem in practical missions, such as farming and emergency response. In recent years, various trajectory planning and tracking algorithms have been widely developed. However, the environmental complexity and the intruders’ unexpected movement pose difficulties in trajectory planning, especially in ensuring the computation time under the allowable threshold. Moreover, the UGV practical trajectory tracking is a challenging task which demands a desired response within various physical constraints. In this article, a two-stage conflict resolution system is proposed. First, a trial-based forward search (TFS) is used to generate a new trajectory deviating the UGV from the initially generated trajectory by the artificial fish swarm algorithm (AFSA), aiming to avoid the unforeseen intruders (unknown in prior) in real-time. Using these two trajectory planning algorithms alternatively, both global trajectory optimality in a cluttered environment and appropriate maneuvers with respect to unexpected intruders can be achieved. Subsequently, the UGV is modeled according to its kinematic characteristics, and thus a multiconstrained model predictive controller (MMPC) is designed to follow the reference trajectory. The physical constraints are respected by integrating them into the controller. Simulations and experimental results demonstrate that the proposed strategy can guide and control a UGV from the start to the destination safely and smoothly, even in the case of multiple obstacles with constant or varying velocities. Furthermore, the proposed collision avoidance strategy can be extended to other unmanned systems, including unmanned aerial vehicles and unmanned surface vehicles. [ABSTRACT FROM AUTHOR]

An adaptive sliding mode fault‐tolerant control strategy is proposed for a quadrotor unmanned aerial vehicle in this article to accommodate actuator faults and model uncertainties. First, a new reaching law is proposed, with which a sliding mode control (SMC) law is constructed. The proposed reaching law is made up of a sliding variable and the distance between it and a designated boundary layer, and it can effectively suppress the unexpected control chattering while preserving the necessary system tracking performance. Then, an adaptive SMC scheme is proposed to further solve the fault and uncertainty compensation problem. The proposed adaptation law helps to prevent overestimation of the adaptive control parameters, as well as avoiding control chattering. Finally, a number of comparative simulation tests are carried out to validate the effectiveness and superiority of the proposed control strategy. The demonstrated quantitative comparison results confirm its advantages. [ABSTRACT FROM AUTHOR]

Focusing on a quadrotor unmanned aerial vehicle (UAV), the presence of unintended actuator faults and external disturbances increases the risk of a crash. Although plenty of efforts have been devoted, how to integrate capability analysis into safety control design is still an open issue. This article presents the design of a safety control system for quadrotor UAVs. Firstly, a system capability analysis method based on a differential flatness algorithm is developed, so that the derivatives (i.e., velocity, jerk, and snap) of flight trajectory can be formulated as flat variables. A tradeoff between system capability and permissible flight maneuverability is made to avoid actuator saturation. Secondly, dual-loop nonlinear disturbance observers are exploited to identify the actuator faults and disturbances, which can thereby be handled by a cascade control scheme. Moreover, the trajectory is regenerated online at the expense of degraded flight maneuverability or even emergency landing, in view of the remaining actuator control authority. When comparing to the existing methods, the gap among safety control, trajectory generation, and system capability analysis is bridged to ameliorate practical flight safety. Finally, flight tests are carried out to demonstrate the unique merits of the proposed system. [ABSTRACT FROM AUTHOR]

• Both multiple quadcopters grouped to different blocks and one block are considered. • Spraying time is reduced by 10.8% and 13.2% in simulation and actual flight tests. • The optimization complexity of one quadcopter's path planning is significantly reduced. • A simulation prototype by MATLAB GUIs and Stateflow is built for mission planner. As the approaching of Agriculture 4.0 era and advancements of new technologies of Unmanned Aerial Vehicles (UAVs) and particularly quadcopter UAVs, plant protection quadcopters are becoming increasingly popular and practical in pesticide spraying, fertilization, pollination, seeding, and other agricultural activities. One of the main problems for plant protection quadcopters is completing planning tasks efficiently and quickly. Therefore, this paper proposes an autonomous task assignment and decision-making method for coverage path planning by multiple cooperative quadcopters. The Sequential Quadratic Programming (SQP) method is adopted to acquire the optimal solution for the proposed problem. Then, a simulation platform by MATLAB Graphical User Interfaces (GUIs) is established using the Stateflow technique, and multiple ZY-UAV-680 quadrotor UAVs are employed to carry out the actual flight tests. Finally, simulations and actual flight tests are conducted to demonstrate the effectiveness of the autonomous optimal task assignment and decision-making method. The final results show that: the proposed method can divide multiple UAVs reasonably to several blocks; the time differences between the simulation test and real flight test are only 39.8 sec and 20.6 sec, and accounts for 6.6% and 3.9% of the spraying time spent on real flight test by three cooperative quadrotors, respectively; the optimal scheme can save 60.8 sec and 80.0 sec in the simulation tests and the real flight tests, which accounts for 10.8% and 13.2% of the time spent on average scheme, respectively. Therefore, it can be concluded that the simulation results can match the real flight test results quite well regardless of average scheme or optimal scheme, and the optimal scheme is more efficient and timesaving than the average scheme. [ABSTRACT FROM AUTHOR]