动态定量称量包装系统BP神经网络PID控制算法

目的针对动态定量称量包装控制系统具有大惯性、滞后、非线性且无法建立精确数学模型等缺点,研究提高动态定量称量包装系统控制精度的方法。方法提出了一种改进型BP神经网络PID的定量称量包装控制系统,将BP神经网络与PID控制方法相结合,通过神经网络的自学习、加权系数的调整,优化PID控制器参数K_i,K_p,K_d,并将粒子群算法引入到神经网络中作为其学习算法,以有效提高BP神经网络算法的收敛速度。结果仿真和实验结果表明,改进型BP神经网络PID控制响应速度快、超调量较小,系统称量误差得到大幅度减小。结论所述控制方法可以明显提高定量称量控制过程的稳定性、精确性以及鲁棒性。

PID Control Algorithm of BP Neural Network of Dynamic Quantitative Weighing Packaging System

The work aims to research the method to improve the control precision of dynamic quantitative weighing packaging system, with respect to its shortcomings, such as great inertia, hysteresis, non-linearity and inability to establish accurate mathematical model. An improved BP neural network PID of quantitative weighing packaging control system was proposed. By combining BP neural network and PID control method, and adjusting the self-learning and weighting coefficient of neural network, the parameters (Ki, Kp and Kd) of PID controller were optimized and the particle swarm algorithm was introduced into the neural network as its learning algorithm, so as to effectively improve the convergence rate of BP neural network algorithm. The simulation and experimental results showed that, the improved BP neural network PID control was featured by fast response speed, small overshoot and greatly reduced system weighing errors. The proposed control method can obviously improve the stability, accuracy and robustness of the quantitative weighing control process.