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第36届中国控制会议

作者： Huaizhong Hu Yu Li Qingyu Yang Yuanli Cai Institute of Control Engineering Xi’an Jiaotong University

ISBN: (纸本)9781538629185

This paper considers a robust control strategy for main steam temperature system of thermal power plant that is affected by immeasurable bounded disturbances. An output-feedback min-max optimization approach is proposed to obtain a controller which could effectively reject disturbances and stabilize the system. This approach combines moving horizon estimation with model predictive control and is formulated into a min-max optimization. At each sampling time, the optimization criterion is maximized over worst-case estimate of disturbance sequence and minimized over optimal control sequence via a primal-dual interior-point method. In this way, the controller can improve robustness under constrained disturbances. Simulation results verify that the presented approach could provide significant anti-disturbance capability compared with a conventional DMC-PID strategy.

关键词： main steam temperature moving horizon estimation model predictive control min-max optimization robust control

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Modeling of main steam temperature Using an Improved Fuzzy Genetic Algorithm

Modeling of Main Steam Temperature Using an Improved Fuzzy G...

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第三十九届中国控制会议

作者： Chuanliang Cheng Chen Peng De-liang Zeng Tengfei Zhang School of Mechatronic Engineering and Automation Shanghai University School of Control and Computer Engineering North China Electric Power University College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications

main steam temperature is an important parameter in thermal power plants. In order to get a better control effect, an improved fuzzy genetic algorithm（FGA） modelling method is proposed in this paper. Different from traditional identification methods, the improved FGA is more suitable for modeling the dynamic process of the main steam temperature which has the characteristics of strong coupling and nonlinearity. Firstly, to solve the nonlinear problem, a fuzzy thought is introduced to partition the whole operating region into several local regions. Then an improved genetic algorithm（GA） which has better identification capability is proposed to build the linear model in each local region. The nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. The simulation results show that the improved FGA can achieve satisfactory effects even in the case of strong coupling and nonlinearity, which provides theory foundation for other control methods.

关键词： main steam temperature Fuzzy genetic algorithm Scheduling program Local linear models

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Multiple-Model Internal Model Control of main steam temperature with a Hybrid Model Scheduling Strategy

Multiple-Model Internal Model Control of Main Steam Temperat...

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第37届中国控制会议

作者： Weiwei Chen Lihui Cen Ankang Cao School of Information Science and Engineering Central South University

Considering the model mismatch of the main steam temperature system,a multiple-model internal model control algorithm with a hybrid model scheduling strategy is *** hybrid model scheduling strategy is investigated combining with the advantages of the weighting strategy and switching principle,aiming at reducing the long transition time and improve the control *** results illustrate that the hybrid model scheduling strategy could improve the dynamic response as well as reduce the rise time when the main steam temperature system works in a wide range of operating conditions.

关键词： Multiple-model internal model control Hybrid model scheduling strategy main steam temperature Nonlinear systems

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A novel time-span input neural network for accurate municipal solid waste incineration boiler steam temperature prediction

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2021年第10期22卷 777-791页

作者： Qin-xuan HU Ji-sheng LONG Shou-kang WANG Jun-jie HE Li BAI Hai-liang DU Qun-xing HUANG State Key Laboratory of Clean Energy Utilization Institute of Thermal EngineeringZhejiang UniversityHangzhou 310027China Shanghai SUS Environment Co.Ltd. Shanghai 201703China

A novel time-span input neural network was developed to accurately predict the trend of the main steam temperature of a 750-t/d waste incineration *** historical operating data were used to retrieve sensitive parameters for the boiler output steam temperature by correlation ***,the 15 most sensitive parameters with specified time spans were selected as neural network *** external testing set was introduced to objectively evaluate the neural network prediction *** results show that,compared with the traditional prediction method,the time-span input framework model can achieve better prediction performance and has a greater capability for *** maximum average prediction error can be controlled below 0.2°C and 1.5°C in the next 60 s and 5 min,*** addition,setting a reasonable terminal training threshold can effectively avoid *** importance analysis of the parameters indicates that the main steam temperature and the average temperature around the high-temperature superheater are the two most important variables of the input parameters;the former affects the overall prediction and the latter affects the long-term prediction performance.

关键词： Waste incineration grate furnace Neural network Time-span input main steam temperature Prediction

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