DIRECT ADAPTIVE REGULATION OF UNKNOWN NONLINEAR DYNAMICAL-SYSTEMS VIADYNAMIC NEURAL NETWORKS

A direct nonlinear adaptive state regulator, for unknown dynamical sys tems that are modeled by dynamic neural networks is discussed, In the ideal case of complete model matching, convergence of the state to zer o pins boundedness of all signals in the closed loop is ensured. Moreo ver, the behavior of the closed loop system is analyzed for cases in w hich the true plant differs from the dynamic neural network model in t he sence that it is of higher order, or due to the presence of a model ing error term. In both cases, modifications of the original control a nd update laws are provided, so that at least uniform ultimate bounded ness is guaranteed, even though in some cases the stability results ob tained for the ideal case are retained.