Online automatic tuning of a proportional integral derivative controller based on an iterative learning control approach

Tan, Kok Kiong; Zhao, S.; Xu, Jin

doi:10.1049/iet-cta:20050004

Cited by 79 publications

(50 citation statements)

References 8 publications

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“…By comparison, a P-type learning algorithm was presented to adjust the setpoint in combination with the model prediction control (MPC) method for tracking the desired profile, which was successfully used to the control of artificial pancreatic beta-cell [23]. Based on the conventional PID control structure, a parallel learning-type PID was added to improve the setpoint tracking performance without sacrificing the closed-loop stability [24]. An alternative anticipatory-type ILC (A-ILC) was developed to adjust the setpoint in terms of the PID control loop for robust tracking of the desired profile [25].…”

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

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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.

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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

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“…Since then, a number of PID-type ILC methods have been designed in the last three decades, e.g., P-type [12,13,6], D-type [10,14,15] PI-type [16,17], PD-type [18,19], and PID-type [20,21]. In contrast, the local controller for a ILC-based PID control could be P-type [22], PI-type [23,8], PID-type [24], and so on.…”

Section: Introductionmentioning

confidence: 99%

Robust stability analysis for an enhanced ILC-based PI controller

Wang

Yang

Zhao

2013

Journal of Process Control

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“…Among the 16 indirect-ILC-related articles mentioned previously, ILC was used to update the set-point for the local control in only two works. 3,4 In the first study, 3 ILC was used to update the set-point for a PID controller, and then a standard PID with adaptive gain was used to replace the ILC-based PID. In the other work, 4 an anticipatory-type ILC (A-ILC) was used to adjust the set-point for a PID controller, and the proposed scheme was implemented on an X-Y platform.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell

et al. 2009

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A novel combination of model predictive control (MPC) and iterative learning control (ILC), referred to learning‐type MPC (L‐MPC), is proposed for closed‐loop control in an artificial pancreatic β‐cell. The main motivation for L‐MPC is the repetitive nature of glucose‐meal‐insulin dynamics over a 24‐h period. L‐MPC learns from an individual's lifestyle, inducing the control performance to improve from day to day. The proposed method is first tested on the Adult Average subject presented in the UVa/Padova diabetes simulator. After 20 days, the blood glucose concentrations can be kept within 68–145 mg/dl when the meals are repetitive. L‐MPC can produce superior control performance compared with that achieved under MPC. In addition, L‐MPC is robust to random variations in meal sizes within ±75% of the nominal value or meal timings within ±60 min. Furthermore, the robustness of L‐MPC to subject variability is validated on Adults 1–10 in the UVa/Padova simulator. © 2009 American Institute of Chemical Engineers AIChE J, 2010

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Online automatic tuning of a proportional integral derivative controller based on an iterative learning control approach

Cited by 79 publications

References 8 publications

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

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

Robust stability analysis for an enhanced ILC-based PI controller

Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell

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