Active Disturbance Rejection Control for Air-Breathing Hypersonic Vehicles Based on Prescribed Performance Function

Chenyang Xu,Na Lu,Humin Lei

doi:10.1155/2019/4129136

Abstract

Aiming at the longitudinal motion model of the air-breathing hypersonic vehicles (AHVs) with parameter uncertainties, a new prescribed performance-based active disturbance rejection control (PP-ADRC) method was proposed. First, the AHV model was divided into a velocity subsystem and altitude system. To guarantee the reliability of the control law, the design process was based on the nonaffine form of the AHV model. Unlike the traditional prescribed performance control (PPC), which requires accurate initial tracking errors, by designing a new performance function that does not depend on the initial tracking error and can ensure the small overshoot convergence of the tracking error, the error convergence process can meet the desired dynamic and steady-state performance. Moreover, the designed controller combined with an active disturbance rejection control (ADRC) and extended state observer (ESO) further enhanced the disturbance rejection capability and robustness of the method. To avoid the differential expansion problem and effectively filter out the effects of input noise in the differential signals, a new tracking differentiator was proposed. Finally, the effectiveness of the proposed method was verified by comparative simulations.

Highlights

Air-breathing hypersonic vehicles (AHVs) are a new type of aircraft, which fly at speeds greater than Mach 5 at near space altitudes
To improve the performance of the tracking differentiator (TD), an active disturbance rejection control (ADRC) method based on a radial basis function neural network (RBFNN) was proposed [23], which enhanced the robustness and disturbance rejection ability of the method
To design the active disturbance rejection control law, the extended state observer is applied to estimate the uncertainty of the AHV model and the external disturbance

Summary

Introduction

Air-breathing hypersonic vehicles (AHVs) are a new type of aircraft, which fly at speeds greater than Mach 5 at near space altitudes. Aiming at the attitude control problem of AHVs, a robust fuzzy control method based on a nonlinear switching system was proposed [12], which used a fuzzy system to approximate the unknown function of the model and guaranteed the robustness of the control scheme. To improve the performance of the tracking differentiator (TD), an ADRC method based on a radial basis function neural network (RBFNN) was proposed [23], which enhanced the robustness and disturbance rejection ability of the method. Aiming at the elastic body of the AHV model, a prescribed performance fuzzy back-stepping control method and performance neural back-stepping control method were proposed [29, 30], which guaranteed the steady-state performance and dynamic performance of the control system. The effectiveness and superiority of the proposed method was verified through the simulation and comparison

G Figure 1

C C C α3 T α2 T α T

Controller Design

Simulation Results

Conclusions

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