Neurocontrol of Nonlinear Dynamic Systems Subject to Unmeasured Disturbance Inputs

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.

[1]  Lee A. Feldkamp,et al.  Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

[2]  Ronald J. Williams,et al.  Training recurrent networks using the extended Kalman filter , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[3]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[4]  Alexander G. Parlos,et al.  Nonlinear dynamic system identification using artificial neural networks (ANNs) , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[5]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[6]  Lothar Litz,et al.  Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).