New design of two-dimensional model predictive iterative learning control with novel error compensation for batch processes

IF 4.4 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control Pub Date : 2026-01-01 Epub Date: 2025-12-01 DOI:10.1016/j.jprocont.2025.103592

Chonggao Hu , Ridong Zhang , Furong Gao

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引用次数: 0

Abstract

Traditional two-dimensional (2D) model predictive model iterative learning control strategies can only rely on feedback to passively deal with time delays, repetitive disturbances, and non-repetitive disturbances of batch processes. To address these shortcomings, this paper proposes a two-dimensional model predictive iterative learning control strategy using an improved state space model structure with new error compensation (2D-EC-MPILC). Firstly, a two-dimensional extended non-minimal state space (2D-ENMSS) model is established, which can provide more degrees of freedom for controller design. Secondly, a novel error compensation (EC) strategy is proposed to correct the tracking error value of the current batch. The novel 2D-EC-MPILC controller is designed with both additional tuning degrees and batch-wise error correction, ensuring an improved control performance. The proposed algorithm is tested on the holding pressure control system of an injection molding process and the temperature control system of a nonlinear batch reactor.

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批处理误差补偿的二维模型预测迭代学习控制新设计

传统的二维（2D）模型预测模型迭代学习控制策略只能依靠反馈来被动处理批处理过程的时间延迟、重复干扰和非重复干扰。针对这些不足，本文提出了一种基于改进状态空间模型结构和新的误差补偿的二维模型预测迭代学习控制策略（2D-EC-MPILC）。首先，建立了二维扩展非最小状态空间（2D-ENMSS）模型，为控制器设计提供了更大的自由度；其次，提出了一种新的误差补偿策略来修正当前批次的跟踪误差值。新型2D-EC-MPILC控制器设计具有额外的调谐度和批量误差校正，确保了更好的控制性能。在注射成型过程保压控制系统和非线性间歇反应器温度控制系统上对该算法进行了验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Process Control 工程技术-工程：化工

CiteScore

7.00

自引率

11.90%

发文量

159

审稿时长

74 days

期刊介绍： This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.