Neural network based model predictive control for a steel pickling process
Document Type
Article
Publication Date
1-1-2009
Abstract
A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, single-output recurrent neural network subsystem models are developed using input-output data sets obtaining from mathematical model simulation. The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases.
Keywords
Feedforward neural network, Model predictive control, Multivariable systems, Steel pickling process, Acids, Algorithms, Canning, Cellular radio systems, Descaling, Feedforward neural networks, Hydrochloric acid, Mathematical models, Nonlinear systems, Pickling, Predictive control systems, Recurrent neural networks, Sequential switching, Steel, Acid baths, Acid concentrations, Conventional-pi controllers, Feedforward, Industrial systems, Input-output datum, Levenberg-Marquardt algorithms, Model mismatches, Model predictive control algorithms, Multi layers, Multi variables, Multiple inputs, Neural network models, Non-linear dynamics, Optimal controls, Prediction horizons, Process models, Sequential quadratic programming, Set-point tracking, State variables, Subsystem models.
Divisions
fac_eng
Publication Title
Journal of Process Control
Volume
19
Issue
4
Publisher
Elsevier
Additional Information
444PE Times Cited:8 Cited References Count:20