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| 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 |
| 收稿日期： |
| DOI： |
| 关键词: 纸浆浓度 PSD算法 单神经元 增益自调整 s函数 |
| Key Words:pulp consistency PSD algorithm single neuron gain scheduling control s-function |
| 基金项目:江苏高校优势学科建设工程资助项目(PAPD)。 |
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| 摘要: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。 |
| Abstract:Pulp consistency fluctuates unpredictably all the time. At the same time, the stable pulp consistency is an important factor to guarantee the quality of the paper. The model of pulp consistency is characterized by uncertainty, large time-delay and time-variation, so conventional PID is difficult to obtain good control quality. Therefore, the algorithm based on single neuron adaptive PSD was proposed. In this paper, 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-function of this algorithm was called to simulink dynamically. The results indicated that comparing with conventional PID control and ordinary single 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. |
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