Model predictive flight control using adaptive support vector regression

This paper explores an application of support vector regression (SVR) to model predictive control (MPC). SVR is employed to identify a dynamic system from input-output data, and the identified model is used for predicting the future states in the MPC framework. In order to deal with time-dependent perturbations, an online adaptation algorithm is proposed for compensating the error between the actual dynamics and identified model. The convergence property of the adaptation rule is discussed using discrete-time Lyapunov stability analysis. Finally, the proposed approach is applied to identification and flight control of a fixed-wing unmanned aircraft.

• Type: Journal
• Year: 2010
• Where: IJON
• Authors: Jongho Shin, H. Jin Kim, Sewook Park, Youdan Kim
• Keywords: Discrete-time Lyapunov Stability, IJON 2010, Model, Support Vector Regression