Performance evaluation of two industrial MPC controllers

Gao, Jianping; Patwardhan, Rohit S.; Akamatsu, K.; Hashimoto, Yukihiko; Emoto, Genichi; Shah, Sirish L.; Huang, Biao

doi:10.1016/s0967-0661(03)00115-1

Cited by 53 publications

(27 citation statements)

References 14 publications

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“…Consider an industrial 6 · 6 process presented in [4], which has the following pair-wise delay matrix: Using the method discussed in this section, without knowing process models other than the delays we would get…”

Section: Examplesmentioning

confidence: 99%

“…Whether this condition holds can be easily determined by checking the variables of the process. As discussed in [4], CV 2 is temperature, CV 3 internal reflux ratio, MV 2 reboiler stem flow, and MV 3 internal reflux flow. The chance for the condition to be true has the probability !…”

Section: Examplesmentioning

confidence: 99%

“…0. Therefore, the interactor matrix should have a diagonal structure and the complete knowledge of the multi-variate process, which was assumed in [4], is not necessary. In the worst case, if one is not able to determine whether the condition is true, one would at most need to find out the first non-zero impulse response coefficients of four sub-transfer functions, a significant reduction of the a priori knowledge than the complete transfer function matrices.…”

Section: Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Alternative solutions to multi-variate control performance assessment problems

Huang

Ding

Thornhill

2006

Journal of Process Control

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Performance assessment of multi-variate control with minimum variance control as the benchmark requires an interactor matrix to filter the closed-loop output. This is to transfer the coordinate of the original variables into a new one in order to identify the control invariant disturbance dynamics from the first few terms of the closed-loop output Markov parameters. There has been a great deal of interest to simplify this approach, in particular, to find methods that do not need the interactor matrix. With this motivation, this paper explores alternative solutions to multi-variate control performance assessment problems. In particular, we will consider two practical scenarios: (1) known time delays between each pair of inputs and outputs, (2) no a priori knowledge about the process model or time delays at all. Solutions to these two scenarios are proposed. Two data-driven algorithms based on subspace approach are derived for the calculation of performance measures. Several examples illustrate the feasibility of the proposed approaches.

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Section: Examplesmentioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

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Alternative solutions to multi-variate control performance assessment problems

Huang

Ding

Thornhill

2006

Journal of Process Control

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“…Since this ground-breaking study, much research has been focused on the performance assessment of both singleinput single-output (SISO) and multiple-input multipleoutput controllers as well as feedback, forward and cascade controllers, resulting in a large number of publications (Desborough and Harris, 1992;Harris et al, 1996Harris et al, , 1999Harris and Seppala, 2002;Jelali, 2006;Qin and Yu, 2007;AlGhazzawi and Lennox, 2009) in addition to several commercial applications (Desborough and Miller, 2002;Gao et al, 2003). The increasing popularity of model predictive control (MPC) in industrial applications (Qin and Badgwell, 2003) has led to a high demand for the performance monitoring of MPC systems.…”

Section: Introductionmentioning

confidence: 99%

“…Shah et al (2002) proposed the normalised multivariate impulse response (NMIR) plot which provides a graphical measure in terms of settling time, decay rates and other controller performance indicators, while Gao et al (2003) applied this method to evaluate MPC controllers. However, this approach is based on deterministic performance assessment.…”

Section: Introductionmentioning

confidence: 99%

Performance Monitoring of MPC Based on Dynamic Principal Component Analysis

Tian

Chen²,

Cao

et al. 2011

IFAC Proceedings Volumes

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Abstract:A unified framework based on the dynamic principal component analysis (PCA) is proposed for performance monitoring of constrained multi-variable model predictive control (MPC) systems. In the proposed performance monitoring framework, the dynamic PCA based performance benchmark is adopted for performance assessment, while performance diagnosis is carried out using a unified weighted dynamic PCA similarity measure. Simulation results obtained from the case study of the Shell process demonstrate that the use of the dynamic PCA performance benchmark can detect the performance deterioration more quickly compared with the traditional PCA method, and the proposed unified weighted dynamic PCA similarity measure can correctly locate the root cause for poor performance of MPC controller.

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Introduction

2016

Process Control System Fault Diagnosis: A Bayesian Approach

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Performance evaluation of two industrial MPC controllers

Cited by 53 publications

References 14 publications

Alternative solutions to multi-variate control performance assessment problems

Alternative solutions to multi-variate control performance assessment problems

Performance Monitoring of MPC Based on Dynamic Principal Component Analysis

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