Online set-point optimisation cooperating with predictive control of a yeast fermentation process: A neural network approach |
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| Authors: | Maciej ?awryńczuk |
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| Affiliation: | Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland |
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| Abstract: | Online set-point optimisation which cooperates with model predictive control (MPC) and its application to a yeast fermentation process are described. A computationally efficient multilayer control system structure with adaptive steady-state target optimisation (ASSTO) and a suboptimal MPC algorithm are presented in which two neural models of the process are used. For set-point optimisation, a steady-state neural model is linearised online and the set-point is calculated from a linear programming problem. For MPC, a dynamic neural model is linearised online and the control policy is calculated from a quadratic programming problem. In consequence of linearisation of neural models, the necessity of online nonlinear optimisation is eliminated. Results obtained in the proposed structure are comparable with those achieved in a computationally demanding structure with nonlinear optimisation used for set-point optimisation and MPC. |
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| Keywords: | ASSTO, adaptive steady-state target optimisation LSSO, local steady-state optimisation LSSO+MPC-NO, the ideal classical multilayer structure with nonlinear set-point optimisation repeated at each sampling instant and the MPC algorithm with nonlinear optimisation LSSO100+MPC-NO, the realistic classical structure with nonlinear set-point optimisation repeated 100 times less frequently than the MPC algorithm with nonlinear optimisation LSSO100+ASSTO+MPC-NPL, the structure with the ASSTO layer and the computationally efficient MPC-NPL algorithm, the LSSO layer is executed 100 less frequently than the MPC-NPL algorithm MFLOPS, millions of floating point operations MPC, model predictive control MPC-NO, model predictive control with nonlinear optimisation MPC-NPL, model predictive control with nonlinear prediction and linearisation SSE, sum of squared errors ss, steady-state SSTO, steady-state target optimisation |
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