Support Vector Regression-Based Internal Model Control

This paper proposes a design of internal model control systems for process with delay by using support vector regression（SVR）.The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle.Closed-system stability and steady error are analyzed for the existence of modeling errors.The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.