Adaptive terminal super-twisting prescribed performance controller for near-space vehicle based on data-driven model

Abstract

A data-driven adaptive terminal super-twisting prescribed performance controller (DASTPC) is designed for near-space vehicle (NSV) to satisfy transient and steady-state performance, and prevent scramjet choking. Firstly, a novel predetermined-time performance function is proposed to guarantee that tracking error can converge to a prescribed bound of small residual sets at the predetermined time. Compared with traditional performance functions, the predetermined-time performance function can achieve faster respond speed, realize more accurate convergence, and avoid overlarge initial value of actuators. Secondly, by combining the predetermined-time performance function with sliding mode control, a novel non-singular fast terminal sliding surface and an improved adaptive super-twisting reaching law are proposed to improve computational efficiency and accelerate convergent rate of system. The adaptive reaching law can avoid excessive gains and attenuate chattering by automatically tuning control gain. Thirdly, a deep recurrent neural network-based long-short term memory (LSTM) is employed to learn time-series historical flight dynamics data offline, so as to construct a data-driven LSTM training model. This data-driven model replaces nominal dynamics model of NSV in DASTPC, effectively suppressing model uncertainties. In addition, a homogeneous high-order sliding mode observer is utilized to compensate for external disturbances, avoiding excessive parameter estimation. Since boundary conditions of the predetermined-time performance function are fully satisfied, the DASTPC can effectively restrict amplitude of angle of attack, thus ensuring the intake condition of scramjet. Ultimately, to illustrate the superiority of DASTPC, several sets of simulations are performed on NSV subject to prescribed performance bound, external disturbances and parameter perturbations.