Application of Model Predictive Control (MPC) to Longitudinal Motion of the Aircraft Using Polynomial Chaos

K. D.R. Dewi, K. Fahim, S. Subchan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Dynamical systems can be stochastic or uncertain because of some assumptions or distractions that limit the problem. This occurs when the system is obtained from data using system identifiers with various uncertainties. One example of a system that contains uncertainty parameters is the longitudinal motion of the aircraft model. The longitudinal motion of the aircraft requires control, so in this study, control was applied using the Model Predictive Control (MPC) method. Before applying control to the aircraft model, the Polynomial Chaos expansion will be applied to the state space model to get the deterministic model. The simulation uses different prediction horizons (Np) and polynomial orders (r). Based on the simulation results, it was found that the pitch rate output can approach the given pitch rate reference.

Original languageEnglish
Pages (from-to)487-498
Number of pages12
JournalNonlinear Dynamics and Systems Theory
Volume23
Issue number5
Publication statusPublished - 2023

Keywords

  • hermite polynomial
  • model predictive control
  • polynomial chaos

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