A Model Predictive Controller Using Multiple Linear Models for Continuous Stirred Tank Reactor (CSTR) and ITS Implementation Issue

Author(s):  
Vivek Agarwal ◽  
Manish Gupta ◽  
Umesh Gupta ◽  
Rahul Saraswat
2012 ◽  
Vol 550-553 ◽  
pp. 2908-2912 ◽  
Author(s):  
Ginuga Prabhaker Reddy ◽  
G. Radhika ◽  
K Anil

In this work, a Neural network based predictive controller is analyzed to a non linear continuous stirred tank reactor (CSTR) carrying out series and parallel reactions: A→B→C and 2A→D. In the first step, the neural network model of continuous stirred tank reactor is obtained by Levenburg- Marquard training. The data for the training the network is generated using state space model of continuous stirred tank reactor. The neural network model of continuous stirred tank reactor is used in model predictive controller design. The performance of present neural network based model predictive controller (NNMPC) is evaluated through simulations for servo & regulatory problems of CSTR. The performance of neural network based predictive controller is found to be superior than conventional PI controller for setpoint tracking problems.


2010 ◽  
Vol 13 (1) ◽  
pp. 16-23
Author(s):  
Tuan Quang Tran ◽  
Minh Xuan Phan

The paper presents one method to design the Model Predictive Controller based on Fuzzy Model. The Plant is simulated by Takagi-Sugeno Fuzzy Model and the Optimisation Problem is solved by the Genetic Algorithms. By using the Fuzzy Model and Genetic Algorithm this MPC gives better quality than the other General Predictive Controllers. The case study of a continuous stirred tank reactor (CSTR) control is presented in this paper.


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