Summary
This study proposes an improved adaptive fault diagnosis and compensation scheme for multisensor faults of hypersonic flight vehicles (HFVs). The faults are detected and isolated through a series of sensor output residuals and thresholds that consider observation error and disturbances. Via an adaptive augmented observer, the faults are estimated accurately and a time‐varying disturbance is handled by an additional differential part. Sensor faults are compensated on the basis of estimation results, and disturbances are considered in the fault‐tolerant control (FTC) design, thereby improving the tracking accuracy of the altitude and velocity and robustness with respect to external disturbances for HFVs. The stability of diagnosis and FTC is analyzed by Lyapunov theory. Numerical simulation results explain the validity of the proposed diagnosis and compensation methods.
This study presents an adaptive fault estimation and model predictive fault-tolerant control (FTC) for the hypersonic vehicle engine with non-Gaussian uncertain output jet plume. The non-Gaussian probability density function (PDF) describing the plume velocity in a stochastic system is approximated by Type II fuzzy radial basis functions. In the fault observer, the novel prey adaptive estimation method is designed for the valve faults with different amplitudes. Ultimately, this fuzzy-prey fusion adaptive observer shields perturbation and accurately estimates complex faults including incipient and intermittent faults. A predictive tolerant controller compensates for all the faults effects and non-fragile compensation factors of FTC eliminate the perturbation interference, thence the output PDF matches with the expected value. Finally, the more refined internal structure of plume output distribution replaces the local and rough valve position variables, helping this study realizes the hypersonic vehicle engine integrated refine control. Simulation result verifies the effectiveness of the proposed approaches and the superiority compared with the existing method.
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