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

Boyang Zhang,Xiuxia Sun,Xiongfeng Deng,Shuguang Liu

doi:10.1155/2019/7272387

Abstract

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.

Highlights

The unmanned quadrotor is a class of pilot-less aerial vehicles which enable to hover, take off, and land vertically with aerodynamic and propulsion characteristics
To enable the recurrent neural network (RNN) model to learn the noisy dynamics in the presence of sensor noises, wind gusts and aerodynamic coupling effects, additive disturbances are included for data generation during the simulation
A novel predictive control scheme based on RNNs was proposed for the multiquadrotor system to achieve trajectory tracking and formation keeping simultaneously

Summary

Introduction

The unmanned quadrotor is a class of pilot-less aerial vehicles which enable to hover, take off, and land vertically with aerodynamic and propulsion characteristics. Due to the capabilities of simplicity, maneuverability, and payload [1, 2], the unmanned quadrotor has been gaining extensive attention and plays an important role in the military and civilian fields, such as real-time reconnaissance, surveillance, search and rescue missions, bush fire monitoring, agricultural crop dusting, and different airborne operations [3,4,5]. These flight missions impose stringent requirements over the modeling and control of quadrotor dynamics and increasingly require multiple quadrotors to coordinate in a formation. The optimality of the path cannot be ensured, as the selection of reactive parameters is aimless or blind

Methods

Results

Conclusion