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In this paper, a robust actuator-fault-tolerant control (FTC) system is proposed for thrust-vectoring aircraft (TVA) control. To this end, a TVA model with actuator fault dynamics, disturbances, and uncertain aerodynamic parameters is described, and a local fault detection and identification (FDI) mechanism is proposed to locate and identify faults, which utilizes an adaptive sliding-mode observer (SMO) to detect actuator faults and two SMOs to identify and estimate their parameters. Finally, a fault-tolerant controller is designed to compensate for these actuator faults, disturbances, and uncertain aerodynamic parameters; the approach combines back-stepping control with fault parameters and a high-order SMO. Furthermore, the stability of the entire control system is validated, and simulation results are given to demonstrate the effectiveness and potential for this robust FTC system.

The longitudinal nonlinear aircraft model with cargo extraction is derived using theoretical mechanics and flight mechanics. Furthermore, the nonlinear model is approximated by a semilinear time-varying system with the cargo disturbances viewed as unknown nonlinearities, both matched and unmatched types. On this basis, a novel autopilot inner-loop based on the LQR andL1adaptive theory is developed to reject the unknown nonlinear disturbances caused by the cargo and also to accommodate uncertainties. Analysis shows that the controller can guarantee robustness in the presence of fast adaptation, without exciting control signal oscillations and gain scheduling. The overall control system is completed with the outer-loop altitude-hold control based on a PID controller. Simulations are conducted under the condition that one transport aircraft performs maximum load airdrop mission at the height of 82 ft, using single row single platform mode. The results show the good performance of the control scheme, which can meet the airdrop mission performance indexes well, even in the presence of±20% aerodynamic uncertainties.

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This article proposes a distributed neuroadaptive monitoring fault-tolerant consensus control scheme for a class of uncertain, nonlinear, strict feedback multi-agent systems which have actuator faults and all the control coefficients in them are unknown. This scheme provides each agent with a local monitor combined with the actuator switching to solve actuator failure. Simultaneously, it guarantees the tracking error satisfies the prescribed transient and steady-state performance, even if there exist actuators switching. Furthermore, the time varying asymmetric Barrier Lyapunov function (BLF) and the auxiliary system are used to analyze input and output constraints' influence. Under the action of aforementioned control scheme, closed-loop systems can be stable and semiglobal uniform boundedness. Additionally, its efficacy can be proved in numerical simulation.