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首页> 外文会议>2010 the 5th IEEE Conference on Industrial Electronics and Applications >Multiple-model predictive control based on fuzzy adaptive weights and its application to main-steam temperature in power plant
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Multiple-model predictive control based on fuzzy adaptive weights and its application to main-steam temperature in power plant

机译：基于模糊自适应权重的多模型预测控制及其在电厂主汽温度中的应用

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摘要

In order to solve the uncertainty and time-varying problems of the complex process during variable operations, a novel multiple-model predictive control system based on fuzzy adaptive weighted is investigated in this paper. Firstly, based on the model-set selecting method for multiple model adaptive control, this system preserves the performance of multiple-model approximating nonlinear property of controlled plant at close quarters, therefore the control system is robust. Secondly, fuzzy adaptive weighted control algorithm is proposed to overcome the output disturbance that caused by model switching. The weighted values are obtained by fuzzy decision-making. In this way the corresponding controller can be switched smoothly when the model is selected through switching. Thirdly, because the output predictive value of practical controlled plant is calculated by weighted average of respective sub-models output probabilities, it can rapidly image the variation of plant characteristic. Meanwhile, the output value of controller is optimized by dynamic matrix control algorithm, so the system has better dynamic performance. Furthermore, because of the convenient design and good real-time performance, this algorithm is of great significance and practical engineering value. This algorithm is applied to the main-steam temperature of a supercritical 600MW Once-through boiler; simulation experiments demonstrate the feasibility and good performance of the proposed approach compared to the former approaches.
机译：为了解决复杂过程的不确定性和时变问题，研究了一种基于模糊自适应加权的新型多模型预测控制系统。首先，基于用于多模型自适应控制的模型集选择方法，该系统保留了近距离控制植物的多模型近似非线性特性的性能，因此该控制系统具有鲁棒性。其次，提出了模糊自适应加权控制算法，以克服模型切换引起的输出扰动。加权值是通过模糊决策获得的。这样，当通过切换选择模型时，可以平滑地切换相应的控制器。第三，由于实际受控植物的产量预测值是通过各个子模型输出概率的加权平均值计算得出的，因此可以快速反映植物特征的变化。同时，通过动态矩阵控制算法对控制器的输出值进行优化，使系统具有较好的动态性能。此外，由于设计方便，实时性好，该算法具有重要的意义和实用的工程价值。该算法适用于超临界600MW直流锅炉的主蒸汽温度。仿真实验证明了该方法与前一种方法相比具有可行性和良好的性能。

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