Abstract
Nonlinear black-box modeling techniques are opening new horizons for modeling and control of nonlinear processes. These kind of models can be used in Model Based Predictive Control (MBPC). These techniques include Wiener Models, Fuzzy Modeling, Recurrent and Feedforward Neural networks and combinations of these. In MBPC, a process model is used to predict process response to alternative controller outputs. There are practically no restrictions with respect to the model structure, so that MBPC can very well deal with process nonlinearities. Model-based predictive control has become an important research area of automatic control theory and, moreover, it has been accepted also in industry [3]. A number of successful applications to industrial processes based on linear techniques has been reported, see [3] for a survey. The ability to handle input and output constraints straightforwardly is one of the reasons for this success.
Submitted to 3rd SNN Neural Network Symposium, September 14–15, 1995 Nijmegen, The Netherlands
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te Braake, H.A.B., Verbruggen, H.B., van Can, H.J.L. (1995). Nonlinear Predictive Control with Neural Models. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_45
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_45
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