摘要
针对纸浆浓度控制系统具有大纯滞后和模型不确定的特性,提出将神经元PID控制和Smith预估控制相结合的控制策略。该控制系统既具有Smith预估补偿控制的优点又有神经元自学习、自适应的能力。仿真实验结果表明该方法可以提高系统的控制品质,证实了这种方法的可行性。
To cope with the problems of uncertainty and delay for pulp concentration control, neuron PID control method combined with Smith predictor is proposed. It is advantageous in compensating the defect of Smith Predictor, making full use of the selflearning and adaptive ability of neuron control. Simulation results indicate that the proposed method is feasible, capable of improving the control system performance.
出处
《大连轻工业学院学报》
2003年第3期214-217,共4页
Journal of Dalian Institute of Light Industry
