Aircraft formation control method

Abstract

The invention provides an aircraft formation control method. The method comprises the steps: establishing a leader-follower relative motion model, and controlling the flight of each follower by adopting the three-axis acceleration of the follower under a leader coordinate system as a control quantity, so that the distance between the current position of each follower and the respective corresponding expected position is smaller than a set value, and the followers fly according to a preset formation to realize formation. Therefore, the formation flight of the aircrafts can be effectively realized, and the effect of distance constraint between the aircrafts can be met.

Description

Technical field [0001] The invention belongs to the technical field of aircraft flight strategies, and in particular relates to an aircraft formation control method. Background technique [0002] At present, the formation control problems of unmanned aerial vehicles mainly include the formation method based on the leader-slave mode and the formation method based on the distributed communication mode. For example, based on the ant colony algorithm, the relative path of the aircraft is planned and then tracked by designing the trajectory tracking control rate. However, this algorithm divides the formation and reconstruction problem into two parts: path planning and trajectory tracking, which is less efficient; or, The control function and time are parameterized and discretized respectively, and the improved genetic algorithm and pigeon swarm algorithm are respectively used for optimization. However, such methods are computationally expensive and are only suitable for two-dimensiona...

AI Technical Summary

Problems solved by technology

For example, based on the ant colony algorithm, the relative path is planned for the aircraft and then tracked by designing the track tracking control rate, but this algorithm divides the formation formation and reconfiguration into two parts: path planning and track tracking, and the solution efficiency is low; or, The control action and time are parameterized and discretized respectively, and the improved genetic algorithm and pigeon group algorithm are used for optimization respectively. However, such methods have a large amount of calculation and are only applicable to two-dimensional planes. Effective verification; Finally, the particle swarm optimization algorithm can also be used to solve the PI controller parameters. Although effective formation control is achieved, this method does not consider the energy consumption problem in the formation reconstruction process

In addition, compared with the multi-objective optimization algorithm, the solution result of the single-objective optimization algorithm is greatly affected by the weight of the cost function. When the formation formation time or energy consumption index changes, the optimal solution needs to be recalculated, which is inefficient.

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