Indirect control of a class of nonlinear dynamic systems

Identification and control designs are considered using neural networks for a class of nonlinear partially known dynamic systems. Real-time implementation of two designs, a neural identifier and a proposed neural controller, using an experimental system, comparisons with two other neural networks as well as conventional schemes, and an implementation architecture are reported. The proposed control design facilitates incorporation of available knowledge about the structure of the system. The study also illustrates the inherent capability of neural networks to handle nonlinearities and perform control effectively for a real world system, based on minimal system information.