Self-adaptive multivariable fixed time preset control method for reusable vehicle

Abstract

The invention relates to the technical field of aerospace aircraft control, and aims to adjust the convergence time of an RLV reentry attitude tracking error under the condition that an aircraft is interfered by unknown matching and non-matching of the upper bound, ensure that the attitude tracking error can be converged in expected time and improve the rapid maneuvering capability and robustness of the RLV. The invention discloses a self-adaptive multivariable fixed time preset control method for a reusable launch vehicle, and the method comprises the following steps: 1, building an RLV attitude dynamics model: building an RLV attitude dynamics equation with matching and non-matching interference and an attitude tracking error dynamic equation; a second part, designing a preset performance controller of the outer ring subsystem; and a third part, designing a preset performance controller of the inner ring subsystem: by designing a control moment, realizing that the attitude angular rate can stably track the virtual control quantity designed by the outer ring subsystem under the condition that the upper bound unknown interference influence exists. The method is mainly applied to aerospace craft control occasions.

Description

technical field [0001] The invention relates to the technical field of aerospace vehicle control, in particular to the field of attitude tracking control of a reusable vehicle. Specifically, it relates to an adaptive multivariable fixed-time preset performance attitude tracking control method for a reusable vehicle. Background technique [0002] In recent years, with the rapid development of space transportation, reusable launch vehicle (RLV, Reusable Launch Vehicle) has attracted more and more attention. RLV is a system that can launch payloads into space multiple times, which can greatly reduce the cost of entering space. It plays an important role in the development of space transportation systems and has important civilian and military values. The RLV model has the characteristics of high nonlinearity, strong coupling characteristics, uncertainty, fast time-varying, non-minimum phase, etc., and the flight environment is complex, which puts extremely high requirements on...

AI Technical Summary

Problems solved by technology

The fuzzy control method has a good effect on dealing with model uncertainty and external disturbances with unknown upper bounds. However, the fuzzy controllers based on fuzzy control design are usually relatively simple and difficult to adapt to the control requirements of complex systems.

Neural network control can approach the RLV time model in real time, thereby improving the robustness of the control system, but usually introduces more control parameters, thereby reducing the computational efficiency of the RLV system

4) Preset performance control: Although the existing RLV attitude control methods can better ensure the stability and robustness of the attitude control of the aircraft, there are often problems such as ignoring the quality of overshoot and steady-state error.

[0005] At present, from the current research status of RLV control, whether it is a linear control method or a nonlinear control method, the RLV attitude controller can only achieve asymptotic convergence or finite time convergence of the attitude tracking error, and cannot predefine the convergence time to achieve a fixed time convergence

Secondly, in the existing control algorithms, generally only the RLV attitude controller design problem in the presence of matching disturbances is considered, and the upper bound of the disturbance must be known

In addition, existing RLV attitude controllers usually only consider the steady-state performance of the system, ignoring the dynamic performance

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