Radial basis function neural networks in variable structure control of a class of biochemical processes

Biochemical processes often display a complicated dynamic behavior, the detailed understanding of which frequently constitutes a barrier between the theoretical foundations and practical implementations. One way of handling the complexity is to use intelligent approaches in the design of controllers. The paper presents an analytic approach to design controllers based on radial basis function neural networks (RBFNN) with particular emphasis on the extraction of the error measure to be used in parameter tuning. The simulation studies stipulate that the control system exhibits a highly robust behavior against disturbances and sharp changes in the command signal. The most important contribution of the paper is that the method presented does not require the analytical details describing the plant dynamics available.