Application of Model Predictive Control Based on BPNN to Heating Boiler

Abstract:

This paper presents the algorithm of model predictive control (MPC) based on BP neural network to the burden system of the heating boiler. Because the burden system of the heating boiler is complex, the proposed approach uses steady, effective way to control the boiler. There is a closed-loop, repeating online optimization, model-based control algorithm which deals with the feedback information and the quantity of the fuel entering the boiler by the way of multi-step future predicting and compensating based on BP neural network. By simulation, it is demonstrated that the burden system of the heating boiler using MPC as control method is better in performance than the traditional PID. Besides, it is compliant to the model of the controlled object, especially to those which parameters of the model are variable.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 2242-2248
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.2242
• Online since: November 2011
• Authors: Yan Ping Wang
• Keywords: BP Neural Network (BPNN), Dynamic optimization, MPC, Prediction Model

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