Use of an artificial immune network optimization approach to tune the parameters of a discrete variable structure controller

Recently, the discrete variable structure controllers (VSCs) for nonlinear systems has received some attention. These VSCs are formulated for the systems with different kinds of uncertainties using switching or nonswitching types of techniques. The performance of these controllers depends not only on the control structure but also on the values of the controller’s parameters. In this context, an artificial immune system (AIS) can be useful to tune the VSCs’ parameters. AISs represent a field of biologically inspired computing that attempts to exploit theories, principles, and concepts of modern immunology to design immune system-based applications in science and engineering, mainly in learning and optimization applications. This paper presents an AIS approach applied to optimize the design of an incremental discrete VSC for minimizing the generalized minimum variance strategy. Control design and implementation tests are assessed in a reactor and a control valve.