Methods and properties of quadratic iterative learning control for semi-conductor processes under different perturbations

Hui-Yong, Kim; Park, Junhyung; Lee, Kwang Soon

doi:10.1109/iccas.2013.6703932

Cited by 2 publications

(2 citation statements)

References 5 publications

(2 reference statements)

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“…A more effective Q-ILC algorithm was proposed in [23], taking the adjacent input variations into account. In article [24], different types of stochastic disturbance are considered in the bias in a linear static model, and Q-ILC algorithms are derived. On the other hand, since uncertainty is widely present in robot system, the Kalman filter was typically considered to enhance the ILC performance [25]- [27].…”

Section: Introductionmentioning

confidence: 99%

Estimation-Based Quadratic Iterative Learning Control for Trajectory Tracking of Robotic Manipulator With Uncertain Parameters

Zhu

2020

IEEE Access

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In this paper, we consider iterative learning control for trajectory tacking of robotic manipulator with uncertainty. An improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic manipulator. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. To deal with uncertainty, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are incorporated for estimation of uncertain parameters by constructing extended system states. The performances between the two filters are also compared. Simulations on a 2DOF Robot manipulator demonstrate that the improved Q-ILC with parameter estimators can achieve faster convergence and better transient performance compared to the original Q-ILC, in the presence of measurement noise and model uncertainty.

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Section: Introductionmentioning

confidence: 99%

Estimation-Based Quadratic Iterative Learning Control for Trajectory Tracking of Robotic Manipulator With Uncertain Parameters

Zhu

2020

IEEE Access

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“…Iterative learning control (ILC), originally proposed to control robot arms by Arimoto, is an advanced control strategy which mimics the human learning mechanism and, therefore, has the capability of improving the control performance from trail to trail without much requirement for prior process knowledge. Due to good control performance and applicability, this control scheme has been studied extensively in recently decades − with its application extending from mechanical systems to many batch or repetitive processes, such as batch chemical reactors, , biomaterial applications, , semiconductor manufacturing, , pharmaceutical engineering, , polymer processing, , etc.…”

Section: Introductionmentioning

confidence: 99%

Integrated Design Method of a Cascade Iterative Learning Control for the Cascaded Batch/Repetitive Processes

Yang¹,

Cao

et al. 2016

Ind. Eng. Chem. Res.

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In industrial applications, many batch/repetitive systems are constructed by two or more subprocesses connected in serial to perform a given task repetitively or periodically, termed as cascaded batch/repetitive processes. In this paper, by introducing a two-dimensional (2D) cost function, an integrated design method of a novel cascade control scheme is developed for this kind of process under the 2D model predictive iterative learning control infrastructure. The resulted cascade control scheme essentially consists of a real-time cascade model predictive control along the time direction as the inner-loop control and an iterative learning control (ILC) along the cycle direction as the outer-loop control. The proposed design algorithm not only realizes an integrated design for all control loops but also guarantees optimal control in terms of the 2D control performance. The convergence criterion of the closed-loop control system along the cycle index is also investigated. Compared to the conventional cascade control schemes, the proposed algorithm is essentially a 2D feedback control with superior control performances which are clearly illustrated by the simulation results on the injection molding process.

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Methods and properties of quadratic iterative learning control for semi-conductor processes under different perturbations

Cited by 2 publications

References 5 publications

Estimation-Based Quadratic Iterative Learning Control for Trajectory Tracking of Robotic Manipulator With Uncertain Parameters

Estimation-Based Quadratic Iterative Learning Control for Trajectory Tracking of Robotic Manipulator With Uncertain Parameters

Integrated Design Method of a Cascade Iterative Learning Control for the Cascaded Batch/Repetitive Processes

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