Cooperative control method of multi-unmanned aerial vehicle formation based on model predictive control

A model predictive control, multi-UAV technology, applied in non-electric variable control, control/regulation systems, 3D position/channel control, etc. The effect of high precision, simple solution process and low computational complexity

Active Publication Date: 2019-06-11
CAS OF CHENGDU INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, at present, there is no outstanding method for formation cooperative control, especially for formation maintenance and t...

Method used

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  • Cooperative control method of multi-unmanned aerial vehicle formation based on model predictive control
  • Cooperative control method of multi-unmanned aerial vehicle formation based on model predictive control
  • Cooperative control method of multi-unmanned aerial vehicle formation based on model predictive control

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Embodiment

[0030] Embodiment: a flow chart of a method for coordinated control of three UAV formations based on model predictive control, including the following steps:

[0031] Step 1. Establish the xoy two-dimensional plane coordinate system. Assume that the performance parameters of each drone are the same, and the parameters of the particle swarm optimization algorithm are the same. For specific parameters, refer to Table 1. pilot drone a 1 The initial position is (25, 0), the target position is (-28, -6); follow the drone's a 2 and a 3 The initial positions are (26, 0), (27, 0) respectively.

[0032] Step 2. According to the environment, mission and UAV-related constraints, the Dijkstra algorithm is used to initially calculate the global cost of the pilot UAV, and then the trajectory planning problem is transformed into a rolling time domain by using model predictive control. The optimization problem is solved by the fast particle swarm optimization algorithm to obtain the next m...

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Abstract

The present invention relates to a cooperative control method of a multi-unmanned aerial vehicle formation based on model predictive control. The method comprises the following steps of I, initializing task requirements, related control parameters and the like; II, conducting preliminary route planning for a pilot unmanned aerial vehicle; III, detecting the condition of an environment of a flightarea in real time through a sensor, judging and selecting a proper flight formation, and calculating (updating) a virtual formation guidance point; IV, calculating a reference value according to the calculated virtual formation guidance point by using the guidance point as a cost, calculating a cost function, and conducting flight control by adopting a particle swarm optimization strategy based ondistributed model predictive control; and V, repeating the step II, the step III and the step IV, and controlling the multi-unmanned aerial vehicle formation to conduct cooperative flight until a target position is reached. The method effectively solves the problem of cooperative control over the formation in a complex environment, and enables a multi-unmanned aerial vehicle system to have stableformation retention capability and efficient formation transformation capability during flight.

Description

technical field [0001] The invention relates to a multi-unmanned aerial vehicle formation cooperative control method based on model predictive control, and belongs to the field of track planning control strategies of multiple aircraft formations considering path information. Background technique [0002] With the maturity of UAV control technology, UAVs have been widely used in military and civilian fields such as battlefield environment reconnaissance, ground target strikes, power line inspections, and aerial photography. At the same time, multi-UAV formation cooperative control has quickly become a frontier field in recent years. The use of multi-UAV formation cooperative flight can make full use of limited single-machine resources to jointly perform large-scale target search, natural disaster disaster acquisition, agriculture and forestry Operations, coordinated attacks and other complex tasks. Therefore, it is of great significance to conduct in-depth research on the co...

Claims

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Application Information

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IPC IPC(8): G05D1/10
CPCG05D1/104
Inventor 秦小林许洋刘佳张力戈
Owner CAS OF CHENGDU INFORMATION TECH CO LTD
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