时变间歇过程的2D-PID自适应控制方法

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。%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.