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论文类型：期刊论文

发表时间：2021-03-05

发表刊物：INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

卷号：52

期号：2

页面范围：263-276

ISSN号：0020-7721

关键字：Iterative learning control (ILC); PID iterative learning control; data driven ILC; optimal ILC

摘要：The paper presents model-free proportional-integral-derivative (PID) type iterative learning control (ILC) approach for the nonlinear batch process. The dynamic linearisation method is considered, which uses the input-output (I/O) measurements to update the model at each iteration. Based on the newly updated model and error information of the previous iteration, optimal PID gains are updated iteratively. The quadratic performance index is employed to optimise the parameters of the PID controller, and then an optimal PID type data-driven iterative learning control (DDILC) scheme is established for nonlinear batch process. The convergence analysis of optimal PID type DDILC is also discussed which can be enhanced by the proper choice of penalty matrices. Simulation examples are also given to demonstrate the effectiveness of the proposed scheme.