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为解决多变量、非线性系统的强耦合问题,提出了降阶与解耦相结合的研究方法。首先用Padé降阶算法进行模型降阶处理,简化了直流炉给水系统的数学模型,进而简化了解耦过程。在此模型基础上采用分散式神经网络开环解耦方法设计神经网络解耦器,将来自其他通道的耦合影响视为干扰进行补偿,进而实现对直流锅炉微过热蒸汽焓值—压力系统的解耦。MATLAB仿真结果表明,该解耦方案是可行的,且可得到较好的解耦效果。
In order to solve the strong coupling problem of multivariable nonlinear systems, a research method of combining reduced order with decoupling is proposed. First, using the Padé reduction algorithm to reduce the model, simplifying the DC boiler feedwater system mathematical model, and then simplify the decoupling process. Based on this model, a decentralized neural network open-loop decoupling method is used to design the neural network decoupler, and the coupling effects from other channels are regarded as interferences to compensate, so as to realize the solution to the micro-superheated steam enthalpy-pressure system of the once-through boiler Coupling. The simulation results of MATLAB show that the decoupling scheme is feasible and the decoupling effect can be obtained.