Altitude and roll control of a hovering quad-rotor air vehicle using the multi-objective approximate optimization of proportional–integral–differential control

Jaehyun Yoon, Jongsoo Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This study explores the optimal proportional–integral–differential (PID) gains of a hovering quad-copter to allow recovery from disturbed altitude and roll positions. Computational fluid dynamics was used to determine the rotor distance and the blade shape parameters for maximizing the hovering thrust. Using a six-degree-of-freedom quad-copter dynamics model, a control algorithm was then used to obtain PID gains. The PID control was approximated using back-propagation neural networks (BPNs). Position control of the quad-copter model was performed by determining the optimal PID gains required to minimize the control duration for altitude and roll. The non-dominated sorting genetic algorithm (NSGA-II) was used for multi-objective optimization and a BPN was used for meta-modelling the PID control. The PID gains generated from bi-objective optimal designs were compared with the initial design. The results confirmed that the recovery time from an unbalanced position was reduced and that the motion of the quad-copter was better stabilized.

Original languageEnglish
Pages (from-to)1704-1718
Number of pages15
JournalEngineering Optimization
Volume49
Issue number10
DOIs
Publication statusPublished - 2017 Oct 3

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Rotor
Rotors
Backpropagation
Optimization
Back-propagation Neural Network
Air
Neural networks
Recovery
Position control
Multiobjective optimization
Sorting
Turbomachine blades
Metamodeling
Position Control
NSGA-II
Sorting algorithm
Dynamic models
Computational fluid dynamics
Shape Parameter
Genetic algorithms

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

Cite this

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abstract = "This study explores the optimal proportional–integral–differential (PID) gains of a hovering quad-copter to allow recovery from disturbed altitude and roll positions. Computational fluid dynamics was used to determine the rotor distance and the blade shape parameters for maximizing the hovering thrust. Using a six-degree-of-freedom quad-copter dynamics model, a control algorithm was then used to obtain PID gains. The PID control was approximated using back-propagation neural networks (BPNs). Position control of the quad-copter model was performed by determining the optimal PID gains required to minimize the control duration for altitude and roll. The non-dominated sorting genetic algorithm (NSGA-II) was used for multi-objective optimization and a BPN was used for meta-modelling the PID control. The PID gains generated from bi-objective optimal designs were compared with the initial design. The results confirmed that the recovery time from an unbalanced position was reduced and that the motion of the quad-copter was better stabilized.",
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