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Learning-based method for controlling unmanned aerial vehicle to complete trajectory tracking under wind disturbance

A trajectory tracking and unmanned aerial vehicle technology, applied in three-dimensional position/channel control, mechanical equipment, combustion engine, etc., can solve problems such as inability to achieve results, poor trajectory tracking accuracy, etc., to maintain fidelity, ensure real-time, The effect of improving efficiency

Pending Publication Date: 2022-04-15
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

He considers the most stringent constraints, a strategy that maintains good constraints but performs relatively poorly in trajectory tracking accuracy due to its conservative nature
Recent methods use the confidence theory of Gaussian process to derive the upper bound of prediction error, so as to ensure stability and safety, but these methods only use stability constraints to achieve trajectory tracking, and cannot achieve good results

Method used

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  • Learning-based method for controlling unmanned aerial vehicle to complete trajectory tracking under wind disturbance
  • Learning-based method for controlling unmanned aerial vehicle to complete trajectory tracking under wind disturbance
  • Learning-based method for controlling unmanned aerial vehicle to complete trajectory tracking under wind disturbance

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0071] Such as figure 1 As shown, a learning-based method for controlling UAV trajectory tracking under wind disturbance includes the following steps:

[0072] Step 1. Input the reference trajectory and UAV state, establish model predictive control based on the prior model, and optimize to obtain the corrected reference trajectory and reference control quantity.

[0073] Input the reference trajectory x, d, input the current state x of the drone; consider a nonlinear affine model

[0074] Transform the UAV system into this form; and linearize it to obtain a linear model, based on which model predictive control is established, that is, at each sampling time t k = t 0 +k×d t , t 0 is the current time, k is the number of prediction steps, and dt is the control interval, to solve a finite time-domain optimal control problem, as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] u 1 (t)∈U

[0081] in To predict the state, the objective function...

Embodiment 2

[0129] This embodiment provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor executes the computer program to realize the learning-based learning described in Embodiment 1. The method of controlling UAV to complete trajectory tracking under wind disturbance.

Embodiment 3

[0131] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the learning-based control UAV described in Embodiment 1 is implemented to complete the trajectory under wind disturbance. method of tracking.

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Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a learning-based method for controlling an unmanned aerial vehicle to complete trajectory tracking under wind disturbance. A Gaussian process model is directly used for compensating wind disturbance, so that the tracking precision is improved; and on the other hand, when the stability and safety constraints are ensured, the Gaussian process prediction error upper bound is utilized to ensure the construction of a high-probability constraint guarantee, and the fidelity of the model can be maintained. And errors are not considered in model prediction control, so that the initiative is maximized, and the tracking precision is further improved. In addition, the complexity of the algorithm is optimized from the structure of the algorithm and model updating, the efficiency of the algorithm is improved, and the real-time performance of the control algorithm is ensured.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle control, and more specifically, relates to a learning-based method for controlling an unmanned aerial vehicle to complete trajectory tracking under wind disturbance. Background technique [0002] UAVs are widely used in many fields to solve complex tasks, including agricultural irrigation, disaster relief, police and military, etc. In many scenarios, drones need to track trajectories accurately in order to complete their tasks. Inaccurate tracking may prevent the mission from being completed or even lead to serious accidents. Therefore, accurate tracking performance is a basic requirement for UAV tracking tasks. Additionally, safety is of paramount importance for dynamic control systems. Violation of safety constraints will not only cause harm to the drone itself, but also to humans in many scenarios. Due to inaccurate tracking caused by wind disturbance, the drone will deviate...

Claims

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Application Information

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IPC IPC(8): G05D1/10
CPCY02T10/40
Inventor 吴挚旋成慧
Owner SUN YAT SEN UNIV
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