Abstract:

To solve the control problems of nonlinear process systems, nonlinear model-predictive control algorithms are studied. pH neutralization process is a typical nonlinear process in chemical process systems. In view of the characteristic of pH neutralization process, the entire model of pH neutralization process system and the inverse model of static nonlinear block are established by neural networks. Then two novel nonlinear predictive control strategies are studied based on model-predictive control and Hammerstein model. The neural networks model predictive control (NNMPC), which is a global solution strategy for nonlinear predictive control systems and nonlinear Hammerstein model predictive control (NLHMPC), which is a strategy based on two steppes separation control are developed and simulated by MATLAB. Control simulation results show that the NNMPC and NLHMPC control strategies have better performances on set-point tracking and anti-interference control response than PID control. They can give effective control performance to nonlinear processes.

Key words: model-predictive control, neural networks, process control, Hammerstein model, pH neutralization process, nonlinear system

摘要：

针对pH中和过程这一化工过程系统中的典型非线性对象特点，应用神经网络建模思想和模型预测控制方法，并结合Hammerstein模型特点，研究pH中和过程非线性系统的两种新型模型预测控制手段，分别建立基于神经网络的非线性预测控制系统整体求解策略和基于Hammerstein模型的两步法预测控制策略，并用MATLAB对其进行仿真。控制仿真结果表明，建立的神经网络预测控制策略和非线性Hammerstein模型预测控制均优于传统PID控制方法，具有良好的设定值跟踪效果和抗干扰控制响应，说明这两种控制策略是非线性过程的有效控制方法。

关键词: 模型预测控制, 神经网络, 过程控制, Hammerstein模型, pH中和过程, 非线性系统