Abstract
In this paper it is proposed a nonlinear approach to model predictive control that is based on a Takagi– Sugeno (TS) fuzzy model representation of a state observer. An industrial evaporator system is taken as an exemplary process and its prediction model is used in the controller. Accurate nonlinear models of the evaporator system components are described. The final model of the evaporator system in state space implementation is used in model based control. The MPC scheme is based on an explicit use of the predictive model of the system response to obtain the control actions by minimizing a cost function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to prescribed constraints. The case study is implemented using MATLAB/Simulink. The simulation results show that the main process variables have good performance and process quality is satisfied.
| Original language | English |
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| Title of host publication | "FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv |
| Pages | 57-62 |
| Publication status | Published - 2013 |
| MoE publication type | A4 Article in a conference publication |
| Event | Food Science, Engineering and Technologies - Plovdiv, Bulgaria Duration: 18 Oct 2013 → 19 Oct 2013 |
Conference
| Conference | Food Science, Engineering and Technologies |
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| Country | Bulgaria |
| City | Plovdiv |
| Period | 18/10/2013 → 19/10/2013 |
Keywords
- process control
- modeling
- optimization
- fuzzy systems
- neural networks
- fuzzy-neural systems