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- D. Williams [3]
- J.B. Gomm [3]
- D.L. Yu [2]
- S.K. Doherty [1]
An investigation into neural network model predictive control is described in this paper. The control strategy developed is applied to a laboratory process to control temperature, pH and dissolved oxygen. The main difficulties in control of this process are nonlinearity, coupling effects among variables and long time-delay in the heat exchanger. Parallel neural models are developed from real process data for the use with online model predictive control and off-line simulations. The online control results are demonstrated. (5 pages)
A simulation study on the control of a multivariable chemical process by using a neural network model predictive control strategy is described in this paper. The laboratory process, in which temperature, pH and dissolved oxygen are involved, has characteristics typical of industrial processes. The main difficulties in control of this process are non-linearity, coupling effects among variables and long time-delay in heat exchange. Neural sub-system models are developed from real process data for the model predictive control strategy and also for use as a bank of parallel models to represent the process in the control simulation. The control simulations are performed before the online control to gain more insight of the process and to determine suitable controller parameters. The simulation results are demonstrated in the paper.
Control of an experimental in-line pH process exhibiting varying nonlinearity and deadtime is described. A radial basis function (RBF) artificial neural network is used to model the nonlinear dynamics of the process. Accommodation of the varying process deadtime in the neural model is achieved by the generation of a feed-forward signal, for input to the neural network, from a downstream pH measurement. The feedforward signal is derived from a variable delay model based on process knowledge and a flow measurement. The neural model is then used to realise a predictive control scheme for the process. Development of the neural process model is described and results are presented to illustrate the performance of the neural predictive control scheme which is tested as a regulator at different setpoints.
