Adaptive support vector regression for UAV flight control.
This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model.
Shin
Jongho
J
School of Mechanical & Aerospace Engineering, Seoul National University, Seoul, Republic of Korea. jh0524@snu.ac.kr
Jin Kim
H
H
Kim
Youdan
Y
D000222:Adaptation, Physiological; D000401:Aircraft / Q000295:instrumentation*; D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012044:Regression Analysis