Development of Model Predictive Control (MPC) on F-16 Longitudinal Motion Using Polynomial Chaos
Kristian Dwi Ratna Dewi(a), Subchan(a), Kistosil Fahim(a)

(a) Mathematics Department, Sepuluh Nopember Institute of Technology
Arief Rahman Hakim Street, Surabaya 60111, Indonesia

Abstract

A dynamic system is a system that changes or experiences dynamics from time to time. In real terms, dynamical systems are not always deterministic. Dynamical systems can be stochastic because of some assumptions or distractions that limit the problem. The uncertainty in the parameters arises when the system parameters are uncertain. This occurs when the system is obtained from data using system identifiers with various uncertainties. One of the methods used to approximate linear dynamic systems with parameter uncertainty is the Polynomial Chaos method. The Polynomial Chaos method transforms the stochastic dynamic system into a deterministic dynamic system with larger state space dimensions. In this study, the control model of the longitudinal motion of the F-16 aircraft was applied using the Model Predictive Control (MPC) method. The F-16 model of longitudinal motion contains uncertain stochastic parameters. Before applying the control method using MPC, the Polynomial Chaos method was applied to the state space model of the longitudinal motion of the F 16 aircraft to obtain the deterministic model. The simulation is implemented using different prediction horizon and polynomial orders. Based on the simulation results, it was found that the pitch angle rate output can approach the given pitch angle rate reference.

Keywords: Longitudinal motion, Polynomial Chaos method, Model Predictive Control (MPC) method, F-16 aircraft motion model

Topic: Mathematics and Statistics