Support vector regression and model reference adaptive control for optimum control of nonlinear drying process, TAPPI JOURNAL February 2016

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ABSTRACT: In this paper, a support vector regression (SVR)-based system identification and model reference adaptive control (MRAC) strategy for stable nonlinear process input-output form is designed. In order to implement the proposed control structure, SVR-based identification methods are clearly addressed. The control of a moisture process on the paper machine illustrates the proposed design procedure and the properties of the SVR-based model identification-adaptive reference model for the nonlinear system. MRAC is widely used in linear system control areas, and neural networks (NN) are often used to extend MRAC to nonlinear areas. Some drawbacks of NN with MRAC are slow speed in learning, weak generalization ability, and a local minima tendency. To overcome this problem, SVR is used instead of NN. With the support vector regressor, a stable controller-parameter adjustment mechanism is constructed by using the model reference adaptive theory. Simulation results show that the proposed approach could reach desired performance.

Application: Paper mills that use the SVR identified MRAC for proper reduction of moisture can achieve less power consumption, more productivity, and improved end-product quality.