基于BP神经网络PID的纸浆浓度自适应控制

Adaptive Control of Pulp Concentration Based on BP Neural Network PID

目的为了克服纸浆浓度控制系统的滞后性、非线性和时变性,以提高纸浆浓度控制性能。方法针对纸浆浓度控制问题提出一种BP神经网络PID控制技术,构建3-4-3的BP神经网络结构,并在该基础上建立BP神经网络PID控制的数学模型,利用BP神经网络实现PID参数的自适应调整。结果仿真结果表明,BP神经网络PID控制相较于传统PID控制收敛速度更快、超调量更小、抗干扰能力更强、鲁棒性更好。结论该控制方法实现了纸浆浓度的自适应控制,为纸浆浓度的最优控制提供了一种有效可行的控制方法。

The work aims to overcome the hysteresis, nonlinearity and time variability of the pulp concentration control system, so as to improve the pulp concentration control performance. A BP neural network PID control technique was proposed with respect to the problem of pulp concentration control. A 3-4-3 BP neural network structure was constructed. Based on that, a mathematical model of BP neural network PID control was built. The adaptive adjustment of PID parameters by BP neural network was made. The simulation results showed that, the BP neural network PID control had faster convergence speed, less overshoot, stronger anti-interference ability and better robustness than the traditional PID control. The control method realizes the self-adaptive control of pulp concentration and provides an effective and feasible control method for optimal control of pulp concentration.