Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle With AOA Constraint and Actuator Faults

Abstract

This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration of angle of attack (AOA) constraint caused by scramjet, the control laws are designed based on barrier Lyapunov function. To deal with the unknown actuator faults, a robust adaptive allocation law is proposed to provide the compensation. Meanwhile, to obtain good system uncertainty approximation, the composite learning is proposed for the update of neural weights by constructing the serial-parallel estimation model to obtain the prediction error which can dynamically indicate how the intelligent approximation is working. Simulation results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

“…Barrier Function. Barrier Lyapunov function (BLF) is used as constraint control, which has been widely used in some fields [55][56][57]. In this paper, we will adopt the following BLF candidate [52]:…”

“…In [54], a BLF is used to constraint the error and integrated into the adaptive backstepping control design to guarantee the states within the barrier. A BLF-based learning control was presented for hypersonic flight vehicle with AOA constraint and actuator faults [55]. In [56], an adaptive BLF controller was devised for PMSM with full-state constraints.…”

Stochastic precision analysis of hypersonic flight vehicle attitude control system in the presence of uncertainties

“…Owing to its bright prospect of civil and military application, HFV has received tremendous attention by worldwide research communities since it appears. The key technologies have been conducted on the material, structure, propulsion system, and controller design (Xu et al, 2019). In contrast with general flight vehicles, the attitude motion of HFV has the stronger nonlinearity, uncertainty, and coupling, which poses a greater challenge to the control system design (Huang et al, 2011).…”

Nonlinear optimal control for autonomous hypersonic vehicles

“…On the other side, hypersonic aerial vehicles are more difficult to be withheld by aerial defense and protection systems, thus giving also a significant advantage in airforce operations [9][10][11][12][13]. The control of hypersonic aerial vehicles is a non-trivial problem due to the multi-variable and strongly nonlinear model of such aircrafts [14][15][16][17][18]. Besides, such systems are underactuated because of having more degrees of freedom than control inputs, and this imposes additional difficulty in achieving flight stabilization and control [19][20][21][22].…”

“…Proof of stability is a main objective in the design of control methods for hypersonic vehicles during the last years. For instance, one can note model-based Lyapunov approaches for hypersonic vehicles control [14,[23][24][25][26][27]. Additionally, one can note model-free Lyapunov approaches for hypersonic vehicles control [28][29][30][31][32][33].…”