2D-PID adaptive control method for time-varying batch processes

doi:10.11949/j.issn.0438-1157.20151861

Abstract

Abstract:

An adaptive control method using the two-dimensional proportional-integral-derivative (2D-PID) iterative learning control (ILC) is proposed for batch processes with time-varying parameters. First, the particle swarm optimization method is utilized to initialize the parameters of 2D-PID. Then, an auto-tuning neuron PID (ANPID) controller is adopted to adaptively tune the process within the batch operation. Moreover, considering the repetitive nature of batch processes, the PID-type ILC is further used to capture the useful information in historical batches. Consequently, the controller performance can be gradually improved batch to batch. The effect of the proposed controller is verified through a simulated batch fermentation process.

Key words: batchwise, process control, neural networks, iterative learning control, particle swarm optimization

摘要：

针对间歇过程存在的参数时变问题,提出一种基于二维PID(2D-PID)迭代学习框架的自适应控制方法。首先,通过粒子群优化算法快速获取初始的2D-PID控制参数。在批次内,采用自调整神经元PID控制器对其进行在线自适应调节。进一步,考虑批次间的重复特性,通过PID型迭代学习控制,以利用历史批次的信息来修正当前批次的调节变量,最终提高控制性能。通过间歇发酵过程的仿真和比较研究,验证了所提出方法的有效性。

关键词: 间歇式, 过程控制, 神经网络, 迭代学习控制, 粒子群算法

CLC Number:

TP273
TQ02

WANG Zhiwen, LIU Yi, GAO Zengliang. 2D-PID adaptive control method for time-varying batch processes[J]. CIESC Journal, 2016, 67(3): 991-997.

王志文, 刘毅, 高增梁. 时变间歇过程的2D-PID自适应控制方法[J]. 化工学报, 2016, 67(3): 991-997.

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