A Practical Approach to Model Based Neural Network Control

This thesis presents a practical approach to model based control of nonlinear dynamical systems using multilayered perceptron type neural networks as process models and controllers. A framework for modeling and identification of nonlinear time series models is introduced. Advanced parameter estimation methods are used to identify the weights of the process model and controller networks. A novel stability analysis and practical methods for maintaining stability and generalization capability are introduced. The identified models are used for model predictive control. Both the direct long range predictive control approach and the dual network control approach are applied. The problems related to the model inverse based IMC design are partially avoided by employing a nonlinear optimal controller within the IMC structure. General guidelines and practical methods for model mismatch and for maintaining stability are introduced and applied. The neuro-control workstation based on HP9000/425 platform and the software tools for identification and control are also introduced. This forms an efficient tool for neural network identification, control design and real-time control tasks. The approach is applied in the control of small laboratory scale water and air heating processes, and in the multivariate control of a pilot headbox of a paper machine. The experimental results show that the proposed approach yields good performance characteristics and robust control.