Fixed-time adaptive neural network unmanned aerial vehicle track angle control method

A neural network and control method technology, applied in the field of fixed-time adaptive neural network UAV track angle control

Active Publication Date: 2019-10-22
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

However, no literature has reported the fixed-time control of the system with dead band and output limitation

Method used

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  • Fixed-time adaptive neural network unmanned aerial vehicle track angle control method
  • Fixed-time adaptive neural network unmanned aerial vehicle track angle control method
  • Fixed-time adaptive neural network unmanned aerial vehicle track angle control method

Examples

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Effect test

Embodiment

[0255] Example: UAV Track Angle Dynamics

[0256] Taking UAV track angle dynamics as an example to illustrate the effectiveness of the above fixed-time adaptive neural network control method in realizing UAV track angle tracking ideal trajectory. The dynamics of UAV track angle can be expressed as:

[0257]

[0258] in and have

[0259]

[0260]

[0261] The system parameters are selected as V t =100m / s,F T =8000N, M=9295.44kg, S=27.87m 2 , I y =75673.6kg·m 2 , ρ=1.7g / L, The dead zone parameter is selected as m r =1,b r = 0.6°, m l =1.05,b l =-0.8°.

[0262] An adaptive neural network control method when a UAV track angle is dynamically fixed in this embodiment comprises the following steps:

[0263] (1) Determine the control target: the reference output signal is selected as y d =(10+2sin(0.5πt))°, the output is limited to |y|≤22°. The control objective is determined as the system output can track the reference output of the system within a fixed...

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Abstract

The invention relates to a fixed-time adaptive neural network unmanned aerial vehicle track angle control method comprising the steps: establishing an unmanned aerial vehicle longitudinal system trackangle dynamic mathematical model and an actuator model with an unknown nonlinear dead zone; determining an ideal output value and an output limit; designing a fixed-time adaptive neural network controller, an adaptive parameter updating law and a fixed-time differentiator so that the output of the system is enabled to track the reference output trajectory within a fixed time while ensuring the boundedness of all the state variables; and performing stability analysis on the control system and determining the parameters of the controller according to the results of stability analysis. The method fully considers the restriction factors such as dead zone, system uncertainty and the output limit existing in the actual system and is applicable to a more general nonlinear system such as a non-strict feedback system and can be better applied to the actual system to ensure the ideal track on the track angle tracking of the unmanned aerial vehicle within the fixed time.

Description

technical field [0001] The invention relates to the field of industrial control, in particular to a fixed-time self-adaptive neural network UAV track angle control method. Background technique [0002] Unmanned aerial vehicles (UAVs) exhibit advantages over traditional aircraft in many aspects and have been used to perform many complex tasks. The automatic flight control system can guarantee the performance of the UAV when the UAV performs special tasks. The complexity and particularity of the UAV's mission puts forward high requirements on the UAV's control time, control accuracy, and system transient and steady-state performance. Due to the complex and changeable flight environment, the UAV system is an uncertain nonlinear system, which has a non-strict feedback structure and is affected by the input dead zone and output limitation, which brings great difficulties to the design of the controller. . [0003] Because the neural network has a good ability to approximate un...

Claims

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Application Information

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IPC IPC(8): G05D1/10G05B13/04
CPCG05D1/101G05D1/0088G05B13/027G05B13/042
Inventor 倪骏康
Owner NORTHWESTERN POLYTECHNICAL UNIV
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