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针对循环流化床锅炉床温的强非线性特性,提出了一种改进的基于多模型的预测控制策略.将多模型控制策略和广义预测控制相结合,采用几个典型工况点的模型来逼近整个运行区间的动态特性,从而取代在线辨识来校正模型.通过跟踪实际工况的变化来对各个子控制器进行加权以获得合适的控制增量,并对广义预测控制的性能指标进行了修改,以消除调节后期的振荡.典型工况点的模型可由离线辨识得到,参数恒定,使得丢潘图方程和控制规律式的求解可以离线进行,降低了在线计算量.将该算法用于某电厂440 t/h循环流化床锅炉的床温控制系统仿真,结果表明:该算法能够很好地克服被控对象的非线性,在负荷大范围变动时仍然可以取得良好的控制效果.
Aiming at the strong non-linearity of bed temperature in circulating fluidized bed boiler, an improved multi-model predictive control strategy is proposed.Multi-model control strategy and generalized predictive control are combined to adopt several models of typical operating conditions Which approximates the dynamic characteristics of the entire operating range to replace the on-line identification to correct the model.We can weight each sub-controller by tracking the changes of actual operating conditions to obtain the appropriate control increment and modify the performance of generalized predictive control , So as to eliminate the post-adjustment oscillation.The model of typical operating point can be obtained by offline identification and the parameters are constant, so that the solution of losing Pan diagram and control law can be performed off-line, reducing the amount of on-line computation.This algorithm is applied to a power plant The simulation results of the bed temperature control system for a 440 t / h circulating fluidized bed boiler show that the proposed algorithm can overcome the nonlinearity of the controlled object well and still achieve good control effect when the load is fluctuating widely.