Designing minimum variance controllers (MVC) for nonlinear systems is confronted with many difficulties. The methods able to identify MIMO nonlinear systems are scarce. Harsh control signals produced by MVC are among other disadvantages of this controller. Besides, MVC is not a robust controller. In this article, the Vector ARX (VARX) model is used for simultaneously modeling the system and disturbance in order to tackle these disadvantages. For ensuring the robustness of the control loop, the discrete slide mode controller design approach is used in designing MVC and generalized MVC (GMVC). The proposed method for controller design is tested on a nonlinear experimental Four-Tank benchmark process and is compared with nonlinear MVCs designed by neural networks. In spite of the simplicity of designing GMVCs for the VARX models with uncertainty, the results show that the proposed method is accurate and implementable.
Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.