| Title |
Distributed Fractional-Order Intelligent Adaptive Fault-Tolerant Formation-Containment Control of Two-Layer Networked Unmanned Airships for Safe Observation of a Smart City |
| ID_Doc |
38911 |
| Authors |
Yu, ZQ; Zhang, YM; Jiang, B; Su, CY; Fu, J; Jin, Y; Chai, TY |
| Title |
Distributed Fractional-Order Intelligent Adaptive Fault-Tolerant Formation-Containment Control of Two-Layer Networked Unmanned Airships for Safe Observation of a Smart City |
| Year |
2022 |
| Published |
Ieee Transactions On Cybernetics, 52, 9 |
| Abstract |
This article investigates a distributed fractional-order fault-tolerant formation-containment control (FOFTFCC) scheme for networked unmanned airships (UAs) to achieve safe observation of a smart city. In the proposed control method, an interval type-2 fuzzy neural network (IT2FNN) is first developed for each UA to approximate the unknown term associated with the loss-of-effectiveness faults in the distributed error dynamics, and then a disturbance observer (DO) is proposed to compensate for the approximation error and bias fault encountered by each UA, such that the composite learning strategy composed of the IT2FNN and the DO is obtained for each UA. Moreover, fractional-order (FO) calculus is incorporated into the control scheme to provide an extra degree of freedom for the parameter adjustments. The salient feature of the proposed control scheme is that the composite learning algorithm and FO calculus are integrated to achieve a satisfactory fault-tolerant formation-containment control performance even when a portion of leader/follower UAs is subjected to the actuator faults in a distributed communication network. Furthermore, it is shown by Lyapunov stability analysis that all leader UAs can track the virtual leader UA with time-varying offset vectors, and all follower UAs can converge into the convex hull spanned by the leader UAs. Finally, comparative hardware-in-the-loop (HIL) experimental results are presented to show the effectiveness and superiority of the proposed method. |
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