Abstract
The flight dynamics of air-breathing hypersonic vehicles (AHVs) is highly nonlinear, multivariable coupling, and includes inertial uncertainties and external disturbances, which require strong robust and high accuracy controllers. Considering the inertial uncertainties and external disturbances, the flight control system of AHVs can be designed as the general control issue for an uncertain nonlinear system with mismatched disturbances. To achieve the target of disturbance rejection, this paper proposes direct and indirect disturbance rejection control approaches to investigate the flight control of AHVs. For different levels of system disturbance, the typical direct and indirect disturbance rejection control schemes are presented and compared with each other. Finally, simulation results illustrate the advantages and application limits of the direct and indirect disturbance rejection control approaches.
Highlights
Air-breathing hypersonic vehicles (AHVs) have attracted lots of attentions for years due to its vast values in both civilian and military applications
Based on the aforementioned analysis, this paper proposes different indirect and direct disturbance rejection control approaches for the flight control of AHVs with respect to different levels of system disturbance
In order to illustrate the control capacities of indirect disturbance rejection control further, all the aerodynamic coefficients are enlarged to have the uncertainties of 30% with respect to nominal values, which is denoted as the case of strong disturbance
Summary
Air-breathing hypersonic vehicles (AHVs) have attracted lots of attentions for years due to its vast values in both civilian and military applications. Preller and Smart [3] had made use of conventional PID feedback to design the flight control system of an AHV Pole placement is another classical method in linear control theories, and is applied widely in engineering applications. Based on LPV model of AHV, Fidan et al [4] employed pole placement method to design the tracking controller, and solved the tracking problem with parameter uncertainties. In [15], the radial basis function neural network (RBFNN) is employed to approach the unknown functions with any desired accuracy This method can be considered as an indirect observation of system uncertainty and external disturbance. Based on the aforementioned analysis, this paper proposes different indirect and direct disturbance rejection control approaches for the flight control of AHVs with respect to different levels of system disturbance.
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