The hypersonic vehicle model is characterized by strong coupling,nonlinearity,and acute changes of aerodynamic parameters,which are challenging for control system design.This study investigates a novel compound contro...The hypersonic vehicle model is characterized by strong coupling,nonlinearity,and acute changes of aerodynamic parameters,which are challenging for control system design.This study investigates a novel compound control scheme that combines the advantages of the Fractional-Order Proportional-Integral-Derivative(FOPID)controller and Linear Active Disturbance Rejection Control(LADRC)for reentry flight control of hypersonic vehicles with actuator faults.First,given that the controller has adjustable parameters,the frequency-domain analysis-method-based parameter tuning strategy is utilized for the FOPID controller and LADRC method(FOLADRC).Then,the influences of the actuator model on the anti-disturbance capability and parameter tuning of the FOLADRC-based closed-loop control system are analyzed.Finally,the simulation results indicate that the proposed FOLADRC approach has satisfactory performance in terms of rapidity,accuracy,and robustness under the normal operating condition and actuator fault condition.展开更多
Aiming at the sensor faults of near-space hypersonic vehicles(NSHV), a fault identification method based on the extended state observer and kernel extreme learning machine(ESO-KELM) is proposed in this paper. The meth...Aiming at the sensor faults of near-space hypersonic vehicles(NSHV), a fault identification method based on the extended state observer and kernel extreme learning machine(ESO-KELM) is proposed in this paper. The method is generated by a combination of the model-based method and the data-driven method. As the source of the fault diagnosis, the residual signals represent the difference between the ESO output and the result measured by the sensor in particular. The energy of the residual signals is distributed in both low frequency bands and high frequency bands. However, the energy of the sensor concentrates on the low-frequency bands. Combined with more different features detected by KELM, the proposed method devotes to improving the accuracy. Meanwhile, it is competent to calculate the magnitude of minor faults based on time-frequency analysis. Finally, the simulation is performed on the longitudinal channel of the Winged-Cone model published by the national aeronautics and space administration(NASA). Results show the validity and the accuracy in calculating the magnitude of the minor faults.展开更多
基金supported by the National HighTech Research and Development Program of China(Nos.11100002017115004 and 111GFTQ2018115005)the National Natural Science Foundation of China(Nos.61473015 and 91646108)the Space Science and Technology Foundation of China(No.105HTKG2019115002)。
文摘The hypersonic vehicle model is characterized by strong coupling,nonlinearity,and acute changes of aerodynamic parameters,which are challenging for control system design.This study investigates a novel compound control scheme that combines the advantages of the Fractional-Order Proportional-Integral-Derivative(FOPID)controller and Linear Active Disturbance Rejection Control(LADRC)for reentry flight control of hypersonic vehicles with actuator faults.First,given that the controller has adjustable parameters,the frequency-domain analysis-method-based parameter tuning strategy is utilized for the FOPID controller and LADRC method(FOLADRC).Then,the influences of the actuator model on the anti-disturbance capability and parameter tuning of the FOLADRC-based closed-loop control system are analyzed.Finally,the simulation results indicate that the proposed FOLADRC approach has satisfactory performance in terms of rapidity,accuracy,and robustness under the normal operating condition and actuator fault condition.
基金supported by the National Natural Science Foundation of China(62073020)。
文摘Aiming at the sensor faults of near-space hypersonic vehicles(NSHV), a fault identification method based on the extended state observer and kernel extreme learning machine(ESO-KELM) is proposed in this paper. The method is generated by a combination of the model-based method and the data-driven method. As the source of the fault diagnosis, the residual signals represent the difference between the ESO output and the result measured by the sensor in particular. The energy of the residual signals is distributed in both low frequency bands and high frequency bands. However, the energy of the sensor concentrates on the low-frequency bands. Combined with more different features detected by KELM, the proposed method devotes to improving the accuracy. Meanwhile, it is competent to calculate the magnitude of minor faults based on time-frequency analysis. Finally, the simulation is performed on the longitudinal channel of the Winged-Cone model published by the national aeronautics and space administration(NASA). Results show the validity and the accuracy in calculating the magnitude of the minor faults.
