基于bp神经网络的锅炉燃烧控制改进动态规划

Baosheng Yang, Xiaoying Yang
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引用次数: 1

摘要

锅炉燃烧过程是一个复杂的系统,具有高非线性、强干扰、强耦合和大滞后的特点,同时其内外扰动非常频繁,且燃料量无法精确测量,采用常规控制方案难以解决锅炉燃烧过程的非线性和长滞后问题。改进动态规划是一种与系统(环境)相互作用,提高控制效果的方法。该方法主要由模型、批评家和行动三个模块组成。本文采用双启发式动态规划方法考虑锅炉燃烧多变量控制系统的求解,实现了锅炉燃烧过程的仿真控制,并分析了该方法的学习能力、控制效果和自适应能力。最后,三种状态都达到了预期的控制目标。
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Improved Dynamic Programming Based on BPNN for Combustion Control of Boiler
As a complex system boiler combustion process has the characteristics--high non-linearity, strong jamming, strong coupling and large lagging, meanwhile its internal and external disturbance are very frequent, and fuel quantity can not be exactly measured, so using conventional control scheme is hard to solve the boiler combustion control problem with non-linearity and long time lags. Improved Dynamic Programming is the method of interacting with the system (environment) and improving control effect. This method mainly consisting of three modules: model, critic and action. This paper uses dual-heuristic dynamic programming to consider the solution of boiler combustion multivariable control system, realize the emulated control of boiler combustion process, and analyze learning capacity, controlling effect and adaptive capability of the approach. Finally all of three states reach the expected control objectives.
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