Interval Type-2 FNN-Based Quantized Tracking Control for Hypersonic Flight Vehicles With Prescribed Performance

Abstract

This paper presents a tracking control scheme with quantization mechanism for hypersonic flight vehicles (HFVs) with prescribed performance using an interval type-2 fuzzy neural network (IT2FNN). A parameterized tracking error model of the HFV is derived with some considered uncertainties, which are approximated by an IT2FNN. The tracking control of the velocity and altitude of the HFV is designed by using a prescribed performance control technique. It allows that transient characteristics of the tracking errors can be improved and adjusted by some prescribed performance functions. According to an adaptive backstepping control design procedure, novel continuous control laws of the fuel equivalency ratio, canard deflection, and elevator deflection are designed with logarithmic quantization mechanism, for the sake of avoiding inadvertently increasing the effective gains of continuous controllers as well as reducing loads of the communication from controller unit to actuator unit. Besides, the limited tracking errors of the flight path angle and angle-of-attack can be achieved by applying the designed controllers. Finally, the presented tracking controllers with quantization mechanism are validated by comparative simulations.