Multivariable identification of an activated sludge process with subspace-based algorithms

Sotomayor, Oscar A.Z.; Park, Song Won; Garcia, Cláudio

doi:10.1016/s0967-0661(02)00210-1

Cited by 41 publications

(20 citation statements)

References 21 publications

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“…Notice however that the first step in the PARSIM-E and dsr e methods are similar. Interestingly Sotomayor et al (2003) have found the dsr.m algorithm to produce the best model on validation data in comparison with four other subspace methods, CCA, MOESP, N4SID and Robust N4SID. The dsr e.m algorithm is a variant of the dsr.m algorithm superior for closed loop identification; both dsr.m and dsr e.m are available in the D-SR Toolbox for MATLAB.…”

Section: On Subspace System Identificationmentioning

confidence: 98%

Modeling, Identification and Control at Telemark University College

Lie¹,

Ruscio²,

Ergon³

et al. 2009

MIC

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Section: On Subspace System Identificationmentioning

confidence: 98%

Modeling, Identification and Control at Telemark University College

Lie¹,

Ruscio²,

Ergon³

et al. 2009

MIC

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“…In said controller, there are two time horizons: one for future outputs predictions (prediction horizon) and another horizon for the past (identification horizon). The horizon for the past defines a sequence of past inputs and outputs, and this sequence is used to reconstruct the present state of the process by applying a subspace-based algorithm [5]. Due to the observability, there is a minimum identification horizon that is necessary to reconstruct the present plant state:…”

Section: Essmpc Controllermentioning

confidence: 99%

Assessment of Model Predictive Control Performance Criteria

Duarte-Barros¹,

Park²

2015

JCCE

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Abstract:The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies: Minimum Variance Control-Harris Index; Statistical Process Control (Cp and Cpk); and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC (model predictive control) algorithms (GPC (generalized predictive control), RMPCT (robust multivariable predictive control technology) and ESSMPC (extended state space model predictive controller)) with different results.

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“…The real process may differ from the process model in two ways: the first, termed parametric mismatch, where the structure of the process model is the same as the true process, but with different parameters; the second, termed structure mismatch, where the structure of the process model differs from the true process. Many non-linear and linear input-output representations are available to model and control non-linear processes, such as, Hammerstein model (Zhu and Seborg [7]), Wiener model (Norquay et al [8]), Non-linear state predictor (NSP) (Wang et al [9]), subspace-based algorithms for linear model (Stomanyor et al [10]), Slident Identification Toolbox for linear multivariable discrete-time system identification (Sima et al [11]). …”

Section: System Identification and Controlmentioning

confidence: 99%