Model Predictive Control for an Aerial Tree-Pruning Robot Based on Alternating Direction Method of Multipliers

Changliang Xu,Qiuyan Zhang,Kaiwen Lu,Huihua Chen,Dongsheng Zhou,Zhong Yang,Luwei Liao,Jiaming Han,Hao Xu

doi:10.1155/2021/9981123

Abstract

Obstacles of some trees within the electric power transmission line channel are of great threat to the electricity supply. Nowadays, the tasks of clearing threatening tree branches are still mostly operated by hand and simple tools. In this article, an aerial tree-pruning robot with a novel structure is designed to improve the pruning operation efficiency and enhance the safety of the staff. However, the long arm of the pruning tool results in much higher rotational inertia of the robot, which brings difficulties for the robot to remain stable. Therefore, a control scheme based on model predictive control is proposed for the aerial tree-pruning robot and to deal with an uncertain system during the pruning operation period. One of the main contributions is that an ADMM (alternating direction method of multipliers) algorithm that solves the constrained QP (quadratic programming) is adopted to implement the model predictive control on embedded computers with limited computational power. The dynamic model of the pruning robot is firstly presented. Then, the control scheme of MPC for the pruning robot is presented. Moreover, the QP problem of robot control is addressed with ADMM. Finally, simulation experiments of attitude tracking as well as the antidisturbances capability verification have been conducted. Results for the system of aerial tree-pruning robot are given to demonstrate the effectiveness of the developed attitude tracking control scheme using ADMM-based MPC.

Highlights

Aerial robots with task tools conducting contact forces with the environment are of typical complex systems, of which the dynamics analysis and control have become emerging hot topics [1]
The tree barriers clean up is the priority among priorities in the operation and maintenance of the electric power facilities
Most of the work has to be implemented by the close cooperation of human and ground equipment

Summary

Introduction

Aerial robots with task tools conducting contact forces with the environment are of typical complex systems, of which the dynamics analysis and control have become emerging hot topics [1]. Model predictive control with sequential linear quadratic (SLQ) solver has been applied to compute feasible and optimal trajectory and to operate a multirotor with a suspended load in dynamic environments [28]. The majority of the optimization problems are tackled with commercial solvers like Gurobi, SNOPT, MOSEK, and so on, which is not suitable for the MPC application on embedded computers which are installed in the aerial robot and responsible for outputting the right command signals to keep the attitude stability and follow the desired trajectories. Motivated by above discussion and analysis, in this paper, we will present an ADMM algorithm that solves a constrained QP (quadratic programming) to implement the Model Predictive Control for the aerial tree-pruning robots. The simulation experimental implementation of the ADMM-based MPC control onto the aerial pruning robot is given

Problem Description

Model Predictive Control of the Aerial TreePruning Robot