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Nonlinear model predictive control using neural networks

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4 Author(s)
Piche, S. ; Pavilion Technol., Austin, TX, USA ; Sayyar-Rodsari, B. ; Johnson, D. ; Gerules, M.

A neural-network-based technique for developing nonlinear dynamic models from empirical data for an model predictive control (MPC) algorithm is presented. These models can be derived for a wide variety of processes and can also be used efficiently in an MPC framework. The nonlinear MPC-based approach presented has been successfully implemented in a number of industrial applications in the refining, petrochemical, pulp and paper, power, and food industries. Performance of the controller on a nonlinear industrial process, a polyethylene reactor, and a simulated continuous stirred tank reactor is presented

Published in:

Control Systems, IEEE  (Volume:20 ,  Issue: 3 )

Date of Publication:

Jun 2000

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