The nature of statistical learning theory
The nature of statistical learning theory
Accurate on-line support vector regression
Neural Computation
Distributed Model Predictive Control: Synchronous and Asynchronous Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Automatica (Journal of IFAC)
Learning and convergence analysis of neural-type structured networks
IEEE Transactions on Neural Networks
A versatile software tool making best use of sparse data for closed loop process control
Advances in Engineering Software
Hi-index | 0.01 |
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 constant and dynamic uncertainties, an online adaptation algorithm is designed using the gradient descent (GD) method and the adjusted SVR model is fed to the MPC optimizer. In addition, the convergence property of the adaptation rule and the condition for the convergence of the MPC optimization are discussed using discrete-time Lyapunov stability analysis. Finally, the proposed approach is applied to identification and flight control of an unmanned aerial vehicle (UAV) lateral dynamics.