Neural network algorithms based methods for adaptive control of complex objects belonging to the class of nonlinear dynamical systems

Applied elements of the complex object control theory are considered when the object is characterized by many state parameters, nonlinear dynamics equations for parameters and the measurement system. The presentation, identification and control of such object (as a nonlinear multi-parametric dynamic system, DS) based on the neural networks methodology are and described compared with those for linear DS. For the case of incomplete information concerning the object model, the use of an adaptive control scheme is demonstrated.