Variable structure systems theory based training strategies for computationally intelligent systems

Abstract

Variable structure systems (VSS) theory, which is particularly well developed for tracking control of uncertain nonlinear systems, has inspired scientists in developing solutions to ill-posed problems like the design of training criteria under a set of conditions and performance metrics. The underlying idea has been to exploit the invariance properties introduced by the theory together with the parametric flexibility of the architectures of computational intelligence. Since the traditional approaches utilizing the gradient information are oversensitive against disturbances, the robustification becomes an inevitable need, and as a powerful tool for handling the nonlinearity, time-delays, saturations and similar system specific difficulties, VSS theory becomes a good candidate for safely expanding the search space. The tutorial focuses on the architectures of common use, and postulates several tuning laws based on the VSS theory.