To address the strong nonlinearity, large uncertainties and multi-source external disturbances during the morphing process, an adaptive optimal control method with performance constraints for hypersonic morphing vehicles is proposed. Firstly, the steady-state controller is designed based on adaptive performance-prescribed control. The singularity problem of fixed performance function is solved by designing an adaptive scaling strategy. Furthermore, the optimal compensation controller is designed based on adaptive dynamic programming. Online optimal control is realized through offline-online policy iteration scheme, where offline policy iteration enhances the stability of the network in the initial stage of online updates, and online policy iteration enhances the robustness of the optimal compensation controller by introducing the zero-sum game. Finally, the stability of the closed-loop system is analyzed based on Lyapunov stability theorems. Simulation results show that the proposed method improves the transient and steady-state performance of the vehicle system. The results also illustrate that the proposed method improves the robustness and adaptability to uncertainties and external disturbances during morphing flight process.

Relative measurements are exploited to cooperatively detect and recover faults in the positioning of Mobile Agent (MA) Swarms (MASs). First, a network vertex fault detection method based on edge testing is proposed. For each edge, a property that has a functional relationship with the properties of its two vertices is measured and tested. Based on the edge testing results of the network, the maximum likelihood principle is used to identify the vertex fault sources. Second, an edge distance testing method based on the noncentral chi-square distribution is developed for detecting faults in the Global Navigation Satellite System (GNSS) positioning of MASs. Third, a recovery strategy for faults in the positioning of MASs based on distance measurement is provided. The effectiveness of the proposed methods is validated by a simulation case in which an MAS passes through a GNSS spoofing zone. The proposed methods are conducive to increasing the robustness of the positioning of MASs in complex environments. The main novelties include the following: (A) network vertex fault detection is based on concrete probability analysis rather than simple majority voting, and (B) the relation of detectability and recoverability of MAS positioning faults with the structure of the relative measurement network is first disclosed.