Distributed Adaptive Fixed-Time Fault-Tolerant Control for Multiple 6-DOF UAVs With Full-State Constraints Guarantee

Abstract

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein material points or only attitude/longitudinal dynamics are considered, this article proposes a distributed fixed-time fault-tolerant control methodology for networked fixed-wing UAVs whose dynamics are six-degree-offreedom with twelf-state-variables subject to actuator faults and full-state constraints. More precisely, state transformations with the scaling function are devised to keep the involved velocity and attitude within their corresponding constraints. The fixed-time property is obtained in the sense of guaranteeing that the settling time is lower bounded by a positive constant, which is independent of initial states. The actuator faults as well as the network induced errors are handled via the bound estimation approach and well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semiglobally uniformly ultimately bounded, and the tracking errors of velocity and attitude converge to the residual sets around origin within a fixed time.

Distributed synchronization control for multiple tailless flying‐wing UAVs with predefined tracking accuracy and input nonlinearities

“…is the smooth function defined as (32). Lumping all of NN weight vectors, approximation errors and filtering error together, the time derivative of (70) along (69) can be computed as…”

“…However, most existing results focus on the control problem of flying‐wing UAV with only attitude dynamics, 7 longitudinal motion 8 or lateral motio 6 . In the above methods the tailless flying‐wing aircraft model is rudely decoupled, which is not suitable for practical application, since the multiple flying‐wing UAVs system is large‐scale nonlinear and strongly coupled. Control accuracy is an important index to guarantee the flight safety of multi‐UAV systems, but most of the existing literature can only ensure that the tracking error converges to an interval defined by a series of unknown constants 14,32,33 . To realize predefined‐accuracy control, by lumping the weight vector and approximation error together, and with the help of the novel smooth switching functions, a performance‐oriented neural control method for multi‐UAV system is proposed in this paper, which guarantees the satisfactory transient tracking error performance and the tracking error converges to a user‐specified interval. Input nonlinearity is a nonnegligible factor affecting control performance of flying‐wing UAV system.…”

“…Control accuracy is an important index to guarantee the flight safety of multi-UAV systems, but most of the existing literature can only ensure that the tracking error converges to an interval defined by a series of unknown constants. 14,32,33 To realize predefined-accuracy control, by lumping the weight vector and approximation error together, and with the help of the novel smooth switching functions, a performance-oriented neural control method for multi-UAV system is proposed in this paper, which guarantees the satisfactory transient tracking error performance and the tracking error converges to a user-specified interval. 3.…”

Robust adaptive dynamic memory‐event‐triggered attitude control for nonlinear multi‐UAVs resist actuator hysteresis

“…The consensus problem of multi‐agent systems (MASs) has aroused extensive attention from many academic communities owing to its potential applications in a great number of areas such as smart power grids, 1‐4 traffic flow control, 5 underwater vehicles, 6 and unmanned air vehicles 7‐9 . The MASs are a class of very important large‐scale intelligent systems, which achieve the overall control goal through coordination among subsystems 10 .…”

“…The consensus problem of multi-agent systems (MASs) has aroused extensive attention from many academic communities owing to its potential applications in a great number of areas such as smart power grids, [1][2][3][4] traffic flow control, 5 underwater vehicles, 6 and unmanned air vehicles. [7][8][9] The MASs are a class of very important large-scale intelligent systems, which achieve the overall control goal through coordination among subsystems. 10 The communication bandwidth resource constraint is always an important factor affecting communication efficiency when the subsystems interact with each other through the communication network.…”