NONLINEAR MODEL PREDICTIVE CONTROL OF AN EVAPORATOR SYSTEM USING FUZZY-NEURAL OBSERVER

Sevil Ahmed, Michail Petrov, Albena Taneva, Yancho Todorov

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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 languageEnglish
Title of host publication"FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv
Pages57-62
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventFood Science, Engineering and Technologies - Plovdiv, Bulgaria
Duration: 18 Oct 201319 Oct 2013

Conference

ConferenceFood Science, Engineering and Technologies
CountryBulgaria
CityPlovdiv
Period18/10/201319/10/2013

Keywords

  • process control
  • modeling
  • optimization
  • fuzzy systems
  • neural networks
  • fuzzy-neural systems

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