Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the format...

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Bibliographic Details
Published in:International journal of aerospace engineering 2019-01, Vol.2019 (2019), p.1-18
Main Authors: Deng, Xiongfeng, Liu, Shuguang, Sun, Xiuxia, Zhang, Boyang
Format: Article
Language:English
Subjects:
Adaptive algorithms
Aerospace engineering
Computer simulation
Evolutionary algorithms
Evolutionary computation
Model accuracy
Neural networks
Optimization
Predictive control
Recurrent neural networks
Rotary wing aircraft
Stability analysis
Subsystems
Tracking problem
Unmanned helicopters
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Summary:This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.
ISSN:1687-5966
1687-5974
DOI:10.1155/2019/7272387