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针对锅炉-汽轮机系统多输入多输出、非线性、强耦合等特点,采用非线性逆系统方法实现反馈线性化和解耦,利用径向基函数(RBF)神经网络方法来辨识逆系统,并通过在线学习减小了建模误差.对解耦后的锅炉-汽轮机系统设计终端滑模控制器,实现了有限时间收敛,采用Lyapunov方法进行了稳定性分析,保证了该控制系统的大范围稳定性.仿真结果表明:该控制系统能够在大范围运行工况下工作良好,优于经典逆系统控制方法设计的系统.
Aimed at the characteristics of multi-input and multi-output, nonlinearity and strong coupling of boiler-steam turbine system, the feedback linearization and decoupling are realized by using the nonlinear inverse system method. The inverse system is identified by the radial basis function (RBF) neural network, Online learning reduces the modeling error.The finite-time convergence is achieved for the decoupled boiler-turbine system by designing the terminal sliding mode controller, and the stability analysis is carried out by Lyapunov method to ensure the large-scale stability of the control system The simulation results show that the control system can work well under a wide range of operating conditions and is superior to the system designed by the classical inverse system control method.