NEURAL-NET-BASED DIRECT SELF-TUNING CONTROL OF NONLINEAR PLANTS

Use of neural networks for direct self-tuning control of stochastic no nlinear plants has been proposed. The control is based upon inverse mo delling of a pseudo-plant. The input to the pseudo-plant is same as th e plant input while its output consists of a linear combination of the plant input and output. The controller is directly identified as a me an square optimal inverse estimator of the pseudo-plant. This approach allows the control of inverse unstable plants. Local convergence prop erties as well as results of simulation studies are presented.