Formation Transformation Based on Improved Genetic Algorithm and Distributed Model Predictive Control

• Published in: Drones (Basel) Vol. 7; no. 8; p. 527
• Main Authors: Chen, Guanyu, Zhao, Congwei, Gong, Huajun, Zhang, Shuai, Wang, Xinhua
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2023
• Subjects: Collision avoidance; Collision dynamics; Communication; Control systems; distributed model predictive control; Drone aircraft; Embedded systems; formation transformation; Genetic algorithms; Matching; Mathematical models; Methods; Optimization; Predictive control; Quadratic programming; target matching; Trajectory planning; Transformations; Unmanned aerial vehicles; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones7080527

In order to solve the problem of multiple aircraft formation transformation to a designated formation, a distributed formation transformation algorithm that decomposes the formation transformation problem into target-matching problems and trajectory-planning problems was studied. According to the actual formation transformation requirements, the target allocation index was proposed, and the improved genetic algorithm which is 23% better than other algorithms was used to achieve target matching. The adaptive cross-mutation probability was designed, and the population was propagated without duplicates by the hash algorithm. The multi-objective algorithm of distributed model predictive control was used to design smooth and conflict-free trajectories for the UAVs in formation transformation, and the trajectory-planning problem was transformed into a quadratic programming problem under inequality constraints. Finally, point-to-point collision-free offline trajectory planning was realized by simulation.