Semi-Empirical Model-Based Multivariable Iterative Learning Control of an RTP System

Cho, Moonki; Lee, Yonghee; Joo, Sangrae; Lee, Kwang Soon

doi:10.1109/tsm.2005.852111

Cited by 35 publications

(17 citation statements)

References 16 publications

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“…Phan and Longman (1988), Moore (1998), Amann et al (1996), LQG-type solutions have been proposed by Lee et al (2001), Cho et al (2005), Tousain et al (2001), Rice and Verhaegen (2010), Ahn et al (2007) for estimating the tracking error and minimizing a quadratic cost function. Work in Bristow et al (2006), Chin et al (2004), Cho et al (2005), Barton et al (2011) has shown that ILC can be applied to systems with underlying feedback loops. The real-time feedback component is intended to reject non-repetitive noise while the ILC adjusts to the repetitive disturbance.…”

Section: Related Workmentioning

confidence: 99%

“…In the second step, the control objective is formulated as a convex optimization problem (Boyd and Vandenberghe 2004). Here, in contrast to least-squares approaches or LQG design (Lee et al 2001;Cho et al 2005;Tousain et al 2001;Rice and Verhaegen 2010;Ahn et al 2007), input and state constraints can be explicitly incorporated. Different (nonlinear) performance objectives can be defined by choosing appropriate vector norms and adequate scaling and weighting of the error vector.…”

Section: Related Workmentioning

confidence: 99%

“…In order to increase the repeatability of the system and decrease the effect of nonrepetitive noise, one may choose to introduce underlying feedback loops, cf. Bristow et al (2006), Chin et al (2004), Cho et al (2005), Barton et al (2011).…”

Section: Advantages and Limitationsmentioning

confidence: 99%

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Optimization-based iterative learning for precise quadrocopter trajectory tracking

2012

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Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance-even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-theart optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arElectronic supplementary material The online version of this article (bitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Optimization-based iterative learning for precise quadrocopter trajectory tracking

2012

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“…Various control methods have been applied to RTP, including decoupling control [1], iterative learning control [2,3], adaptive control [4], internal model control [1], model-based control [5] and nonlinear model predictive control [6]. Huang et al [7] used a proportional-double integral-derivative (PI 2 D) controller to eliminate offset error during the heating step.…”

Section: Introductionmentioning

confidence: 99%

Iterative identification of temperature dynamics in single wafer rapid thermal processing

Cho

Edgar

Lee

2009

Korean J. Chem. Eng.

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As the standard size of silicon wafers grows and performance specifications of integrated circuits become more demanding, a better control system to improve the processing time, uniformity and repeatability in rapid thermal processing (RTP) is needed. Identification and control are complicated because of nonlinearity, drift and the time-varying nature of the wafer dynamics. Various physical models for RTP are available. For control system design they can be approximated by diagonal nonlinear first order dynamics with multivariable static gains. However, these model structures of RTP have not been exploited for identification and control. Here, an identification method that iteratively updates the multivariable static gains is proposed. It simplifies the identification procedure and improves the accuracy of the identified model, especially the static gains, whose accurate identification is very important for better control.

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“…For real-time control of RTP equipment, there have been continuous studies as has been reviewed in Edgar et al [1] Various control methods have been attempted including decoupling control [2], proportional-double integral-derivative control [3], iterative learning control [4][5][6][7][8], adaptive control [9], internal model control [10], LQG control [11], etc. Although PID control is still practiced in some commercial systems, it seems that model-based multivariable control, whatever the law is, has become an indispensable technique to meet the ever tightening uniformity specifications in the present 12-inch and future RTP equipment.…”

Section: Introductionmentioning

confidence: 99%

Combined run-to-run and LQG control of a 12-inch RTP equipment

Won

Yun

et al. 2009

Korean J. Chem. Eng.

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A combined run-to-run (R2R) and LQG control method has been proposed for rapid thermal processing (RTP) equipment for run-wise improvement and real-time multivariable control of the temperature uniformity over the wafer surface. The standard LQG objective was modified to include a quadratic penalty term for input deviation from bias values which are updated by an R2R control law. The proposed method has been applied to commercial 12-inch rotating RTP equipment with four pyrometers and ten circular groups of tungsten-halogen lamps for measurements and manipulation of wafer temperatures. The performance of LQG control was evaluated under wafer rotation and found to show quite accurate tracking. For evaluation of the combined control technique, a wafer with seven thermocouples (TC's) attached along the radial direction has been employed for the TC measurements to be used for R2R control, whereas the pyrometer measurements are fed back for real-time LQG control. It was observed that the temperature uniformity is improved as the run number increases.

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Semi-Empirical Model-Based Multivariable Iterative Learning Control of an RTP System

Cited by 35 publications

References 16 publications

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Iterative identification of temperature dynamics in single wafer rapid thermal processing

Combined run-to-run and LQG control of a 12-inch RTP equipment

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