The nature of statistical learning theory
The nature of statistical learning theory
Neural network based control schemes for flexible-link manipulators: simulations and experiments
Neural Networks - Special issue on neural control and robotics: biology and technology
Shrinking the tube: a new support vector regression algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Optimal control by least squares support vector machines
Neural Networks
Neural Networks - 2005 Special issue: IJCNN 2005
Learning and convergence analysis of neural-type structured networks
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
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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.