Neurocontrol of nonlinear dynamic systems subject to unmeasured disturbance inputs

Abstract

This paper presents a neural network-based approach for estimation of unmeasured disturbance inputs, modeling and control of nonlinear dynamic systems. Some inputs of a dynamic system are assumed to be measurable all the time, while others can only deliver training data for a certain period of time. The unmeasured disturbance inputs are estimated on-line based on a recurrent neural network model of the system and using the extended Kalman filter (EKF). Furthermore, the training of a recurrent neurocontroller is carried out on the basis of the neural model of the system. In addition to the measurable input and output variables of the system, the neurocontroller makes use of the estimated unmeasured disturbance inputs to calculate its control signals. A mathematical model of a drying drum is employed to demonstrate the proposed approach.