Robust iterative learning control with applications to injection molding process

Gao, Furong; Yang, Yi; Shao, Cheng

doi:10.1016/s0009-2509(01)00339-6

Cited by 176 publications

(85 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…37,38 Technically speaking, all of the above control schemes have a common key component-the process model-whose accuracy significantly affects the final performance, even though the proper controller design can partially compensate for a model mismatch. A general system structure of adaptive control of injection velocity is shown in Fig.…”

Section: Machine Controlmentioning

confidence: 99%

“…ILC has been employed to control injection velocity by Gao et al, 37 and to control hydraulic pressure/ram position by Havlicsek and Alleyne, 32 where the traditional openloop feedforward compensator was combined with feedback optimization control to achieve stable convergence and performance improvement akin to closed-loop control. The control law of ILC can be expressed in the following form:…”

Section: Machine Controlmentioning

confidence: 99%

See 1 more Smart Citation

A review of current developments in process and quality control for injection molding

2005

View full text Add to dashboard Cite

ABSTRACT:Injection molding is one of the most versatile and important manufacturing processes capable of mass-producing complicated plastic parts in net shape with excellent dimensional tolerance. Injection molding process and quality control has been an active research area for many years, as part quality and yield requirements become more stringent. This paper reviews the state-of-the-art research and development in injection molding control. It organizes prior studies into four categories, namely, process setup, machine control, process control, and quality control, and presents the distinction and connection of these different levels of control. This paper further reviews and compares the typical variables, models, and control methods that have been proposed and employed for those control tasks. Strictly speaking, real online quality control without human intervention has yet to be realized, primarily due to the lack of transducers for online, real time quality response measurement, and a robust model that correlates the control variables with quantitative quality measurements. Based on the research progress to date, this paper suggests that the different levels of control tasks have to be integrated into a multilevel quality control system, and

show abstract

Section: Machine Controlmentioning

confidence: 99%

Section: Machine Controlmentioning

confidence: 99%

A review of current developments in process and quality control for injection molding

2005

View full text Add to dashboard Cite

show abstract

“…In Ref. [4], a robust ILC strategy in which the boundedinput-bound-output stability is guaranteed was proposed for the injection molding control. Chin et al [5] developed an improved control framework in which the real-time feedback control and ILC are combined for the repetitive processes.…”

Section: Introductionmentioning

confidence: 99%

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

Zou

Zhang

2018

ISA Transactions

View full text Add to dashboard Cite

“…With the wide application of ILC in engineering applications in the recent years, it has become increasingly appealing to develop robust ILC methods to deal with time-varying uncertainties occurring in a cycle or cycle-to-cycle (batchwise) uncertainties, because many batch processes, e.g., industrial injection molding and pharmaceutical crystallization, are slowly varying from batch to batch, while repeating fundamental dynamic response characteristics [1][2][3][4]. As surveyed by Bonvin et al [5], Ahn et al [6], and Wang et al [7], most of existing references have been devoted to time-invariant linear or nonlinear batch processes.…”

Section: Introductionmentioning

confidence: 99%

Robust PID based indirect-type iterative learning control for batch processes with time-varying uncertainties

Liu

Wang

Chen

2014

Journal of Process Control

View full text Add to dashboard Cite

Based on the proportional-integral-derivative (PID) control structure widely used in engineering applications, a robust indirect-type iterative learning control (ILC) method is proposed for industrial batch processes subject to time-varying uncertainties. An important merit is that the proposed ILC design is independent of the PID tuning that aims primarily to hold robust stability of the closed-loop system, owing to the fact that the ILC updating law is implemented through adjusting the setpoint of the closed-loop PID control structure plus a feedforward control to the plant input from batch to batch. According to the robust H infinity control objective, a robust discrete-time PID tuning algorithm is given in terms of the plant state-space model description to accommodate for time-varying process uncertainties. For the batchwise direction, a robust ILC updating law is developed based on the two-dimensional (2D) control system theory. Only measured output errors of current and previous cycles are used to implement the proposed ILC scheme for the convenience of practical application. An illustrative example from the literature is adopted to demonstrate the effectiveness and merits of the proposed ILC method.

show abstract

Robust iterative learning control with applications to injection molding process

Cited by 176 publications

References 9 publications

A review of current developments in process and quality control for injection molding

A review of current developments in process and quality control for injection molding

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

Robust PID based indirect-type iterative learning control for batch processes with time-varying uncertainties

Contact Info

Product

Resources

About