基于单神经元PSD的纸浆浓度控制算法研究

Pulp Consistency Control Algorithm Based on Single Neuron Adaptive PSD

稳定的纸浆浓度是保证纸张质量的重要因素,但是纸浆浓度本身又处于长期不可预测的波动中。针对常规方法无法解决纸浆浓度模型的不确定、大时滞、时变性等特点带来的控制问题,提出了一种单神经元PSD的控制算法。利用增益自调整中的PSD算法改善单神经元响应慢的特性,使其增益具有自调整功能,设计出一种不依赖模型、实时性好的快速自适应控制算法。在Simulink中,调用s函数进行仿真,结果表明,与单神经元控制算法以及常规PID算法相比,改进的PSD控制算法响应速度快,并有较强的抗干扰性和自适应性。THJSK-1平台中的控制研究也表明该算法具有可行性。

Pulp consistency fluctuates unpredictably all the time. At the same time, the stable pulp consistency is an important factor to guar- antee the quality of the paper. The model of pulp consistency is characterized by uneertainty, large time-delay and time-variation, so conven- tional PID is difficult to obtain good control quality. Therefore, the algorithm based on single neuron adaptive PSD was proposed. In this pa- per, the PSD algorithm from the identification free control algorithm was added to improve the response rate of single neuron PID control. Its gain was with self-tuning and thus a model-independent and more real-time adaptive fast algorithm was developed. In the Matlab, the s-func- tion of this algorithm was called to siinulink dynamically. The results indicated that comparing with conventional PID control and ordinal7 sin- gle neuron PID control , the control algorithm had better response rate, stronger interference rejection and the greater adaptive ability. The real-time control on THJSK-1 experiment platform indicated this control algorithm was feasible.