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Real-time robust generalized dynamic inversion based optimization control for coupled twin rotor MIMO system.

Nadir Abbas1, Xuejun Pan1, Abdur Raheem2

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This study introduces a novel robust optimization control law for the Twin Rotor MIMO System (TRMS), effectively managing perturbations and reducing computational load for enhanced stability and performance.

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Area of Science:

  • Robotics and Control Systems
  • Nonlinear System Dynamics
  • Optimization Techniques

Background:

  • Twin Rotor MIMO Systems (TRMS) exhibit complex nonlinear dynamics and are susceptible to various perturbations.
  • High computational cost and potential actuator failure due to chattering phenomena are significant challenges in TRMS control.
  • Existing control methods struggle to simultaneously address nonlinear dynamics, uncertainties, and computational efficiency.

Purpose of the Study:

  • To design a novel robust optimization control law for the TRMS.
  • To mitigate continuous varying perturbations including coupling effects, unknown states, and parametric uncertainties.
  • To reduce computational cost and avoid actuator failure while ensuring robust stability and performance.

Main Methods:

  • Robust Generalized Dynamic Inversion (RGDI) combined with Sliding Mode Control (SMC) and optimization techniques.
  • Lyapunov stability analysis, controllability, and observability matrices for stability augmentation.
  • Development of Euclidean error norm for state deviation estimation.
  • Minimization of perturbations and computational cost using RGDI-based optimization.

Main Results:

  • The proposed RGDI-based optimization controller effectively minimizes perturbations and computational cost.
  • RGDI-based optimization overcomes the chattering phenomena associated with RGDI-based SMC, preventing actuator failure.
  • Guaranteed robust stability and robust performance demonstrated through simulations and real-time implementation.
  • Validation of the novel dynamic approach's effectiveness in a worst-case scenario using MATLAB simulations.

Conclusions:

  • The novel robust optimization control law augmented with RGDI offers a superior solution for TRMS control compared to traditional methods.
  • The controller effectively handles complex nonlinear dynamics and external perturbations while maintaining stability and performance.
  • This approach significantly reduces computational load and enhances actuator reliability, paving the way for practical applications.