Design and real time testing of a neural model predictive controller for a nonlinear system

• Published in: Chemical engineering science Vol. 50; no. 15; pp. 2419 - 2430
• Main Authors: Van Can, H.J.L., Te Braake, H.A.B., Hellinga, C., Krijgsman, A.J., Verbruggen, H.B., Luyben, K.Ch.A.M., Heijnen, J.J.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 1995; Elsevier
• Subjects: Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization; Applied sciences; Chemical engineering; Exact sciences and technology; Design; Process control; Theoretical study; Pressure vessel; SISO system; Heuristic approach; Neural network; Experimental study; Performance; Non linear system; Artificial intelligence; Predictive control
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/0009-2509(95)00083-H

This paper presents the design of a one step ahead model predictive controller based on a neural network for a single input single output nonlinear system. The design procedure is mainly concerned with the identification of the nonlinear system using a neural network. In the absence of a consistent theory for nonlinear systems an heuristic approach is proposed, based on three main subjects: selection of a suitable training signal, calculation of the network parameters and selection of the most appropriate network configuration. The neural network model was trained using a carefully selected training signal that contained all the relevant process and controller dynamics. The parameters of many network configurations, varying in dimension and composition of the input vector and number of hidden nodes were calculated with a fast noniterative method. The predictive power of all these configurations was compared for the one step ahead prediction and for the multistep ahead prediction over the whole horizon of the test set. This way it was possible to make a rational choice for the most appropriate network configuration. The proposed controller based on the obtained network configuration was tested with real time experiments using a pressure vessel. The presented heuristic approach proved to be successful. The controller was capable of tracking various setpoint changes, even under circumstances that were not present during the identification of the model. The performance of the controller was compared with the performance of a conventional PI controller.