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Related Experiment Video

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Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo

Masoud Shirzadeh1, Abdollah Amirkhani2, Nastaran Tork3

  • 1Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15875-4413, Iran.

ISA Transactions
|January 10, 2021
PubMed
Summary

This study introduces an adaptive control strategy using a type-2 fuzzy neural network (T2FNN) and exponential sliding mode control (ESMC) for quadrotor trajectory tracking. The cuckoo optimization algorithm (COA) optimizes parameters, significantly improving performance over other methods.

Keywords:
Cuckoo algorithmFuzzy neural networkQuadrotorTrajectory trackingType-2 fuzzy controller

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Quadrotor trajectory tracking is crucial for autonomous applications.
  • Existing control methods like sliding mode controllers (SMCs) can suffer from chattering.
  • Adaptive fuzzy neural networks offer potential for robust control but require effective parameter optimization.

Purpose of the Study:

  • To propose a novel adaptive and robust control strategy for quadrotor trajectory tracking.
  • To reduce chattering phenomena inherent in traditional SMCs.
  • To optimize control parameters using an advanced metaheuristic algorithm.

Main Methods:

  • A type-2 fuzzy neural network (T2FNN) integrated with exponential sliding mode control (ESMC) was developed.
  • The cuckoo optimization algorithm (COA) was employed to optimize controller gains and sliding surface parameters.
  • Lyapunov stability theory was used for online T2FNN adaptation.
  • Performance was evaluated against genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO).

Main Results:

  • The proposed COA-based ESMC-AT2FNN approach demonstrated superior transient and steady-state trajectory-tracking performance.
  • The method effectively handled uncertainties, external disturbances, and control signal saturation.
  • Statistical analysis confirmed the robustness and convergence of COA, with mean and standard deviation values of 0.00006173 and 0.000092, respectively.

Conclusions:

  • The adaptive T2FNN and ESMC strategy, optimized by COA, provides an effective solution for quadrotor trajectory control.
  • The approach significantly outperforms traditional methods and other optimization algorithms.
  • The study validates the efficacy and stability of COA for complex control optimization problems.