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This paper addresses the fault-tolerant control of hypersonic flight vehicle. To estimate the unknown function in flight dynamics, neural networks are employed in controller design. Moreover, in order to compensate the actuator fault, an adaptive signal is introduced in the controller design to estimate the unknown fault parameters. Simulation results demonstrate that the proposed approach could obtain satisfying performance.

Fault diagnosis and fault-tolerant control have been a hot issue since its significance on actual systems with actuator faults. By identifying or estimating unknown faults, the influence of faults could be considered and eliminated in controller design. Representative study on this issue could be found in [

Currently, most studies on HFV control did not take the actuator fault into consideration. However, unknown fault such as actuator dead-zone is quite common in nonlinear systems and may cause serious consequences [

The model of HFV is highly nonlinear; meanwhile the altitude subsystem could be considered as a strict-feedback system [

The paper is organized in 6 parts. In Section

The COM in [

Consider the following actuator fault model:

In HFV systems, the fault will cause error between designed and actual control input and it will result in tracking error or even flight instability.

The strict-feedback form altitude subsystem based on the hypersonic flight dynamics is considered

In altitude subsystem (

Flight path angle (FPA) tracking error

Choose virtual control of

To avoid the continuous derivative of virtual control, the following first-order differentiator is designed:

Define the tracking error

Choose virtual control of

The following first-order differentiator is designed:

Define the tracking error

Due to the uncertainty caused by imprecise model, the nonlinear function

In order to eliminate the influence of unknown constant

The adaptive law is designed as

In previous work on HFV dead-zone fault control [

Define the velocity tracking error as

Consider the HFV altitude system (

The Lyapunov function candidate is chosen as

The derivative of

According to the conclusion in [

The following inequalities hold:

The derivative of

Let altitude increase

The parameters in the controller are set as

Figure

Altitude.

FPA, pitching angle, and pitching rate.

Response of

NN weights norm.

Control input.

This paper proposes an adaptive back-stepping control law with NN learning for HFV control. The influence of actuator fault is eliminated by constructing an adaptive compensation signal. Meanwhile, the unknown nonlinearity is estimated by neural networks. The simulation results clearly present the consequence of the above design and verify that the approach could reach the desired tracking performance when actuator fault and model uncertainty exist.

The authors declare that there are no conflicts of interest regarding the publication of this paper.