Adaptive deadband control of a drifting process with unknown parameters

Lian, Zilong

doi:10.1016/j.spl.2006.12.006

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Feedback Adjustment Methods1

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2014

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Cited by 2 publications

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“…Box's work on deadband adjustment policies have been extended to the case when the process parameters are unknown and need to be estimated. Bayesian on‐line and non‐Bayesian methods have been proposed. Extensions to the multiple input, multiple output case have also been proposed .…”

Section: Feedback Adjustment Methodsmentioning

confidence: 99%

An overview of George Box's contributions to process monitoring and feedback adjustment

Woodall

2014

Appl Stoch Models Bus & Ind

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Section: Feedback Adjustment Methodsmentioning

confidence: 99%

An overview of George Box's contributions to process monitoring and feedback adjustment

Woodall

2014

Appl Stoch Models Bus & Ind

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“…designed a new Sequential Monte Carlo (SMC) adjustment method to solve the setup adjustment problem under the scenario that the initial bias mean, variance, and measurement error variance are all unknown. Lian and Del Castillo (2007) considered a similar problem except for assuming no initial bias but adjustment error incurred when adjusting x t . And the variance of the adjustment error was also assumed to be unknown in that paper.…”

Section: Introductionmentioning

confidence: 99%

Optimal setup adjustment and control of a process under ARMA disturbances

He¹,

Xie

Wang

2014

IIE Transactions

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Process adjustment uses information from past runs to adjust settings for the next run and bring the output to its target. The efficiency of a control algorithm depends on the nature of the disturbance and dynamics of the process. This article develops a control algorithm when the disturbance is a general ARMA( p, q) process, in the presence of measurement error and adjustment error together with a random initial bias. Its optimality property is established and the stability conditions are derived. It is shown that the popular Exponentially Weighted Moving Average (EWMA) controller is a special case of the proposed controller. In addition, Monte Carlo simulations are conducted to study the finite sample behavior of the proposed controller and compare it with the proportionalintegral-derivative controller when the disturbance is an ARMA(1,1) process and with the EWMA controller when the disturbance is an IMA(1,1) process. The ARMA controller is also implemented to control an ARMA(2,1) disturbance and its performance is compared with the other two controllers. All of the results reflect the new controller's superiority when multiple sources of uncertainty exist or a general ARMA( p, q) disturbance is incurred.

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Adaptive deadband control of a drifting process with unknown parameters

Cited by 2 publications

References 10 publications

An overview of George Box's contributions to process monitoring and feedback adjustment

An overview of George Box's contributions to process monitoring and feedback adjustment

Optimal setup adjustment and control of a process under ARMA disturbances

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