Optimal sensor network design for multirate systems

Kadu, Sachin C.; Bhushan, Mani; Gudi, Ravindra D.

doi:10.1016/j.jprocont.2007.10.002

Cited by 34 publications

(12 citation statements)

References 23 publications

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“…The authors showed that redundancy in measurements improved the estimation accuracy. Kadu et al considered the effect of different sampling frequencies on state estimation accuracy and developed a methodology to solve an implicit multiobjective optimization problem. Conversely, Madron and Veverka adopted a Gauss–Jordan elimination method for achieving observability of all key variables at the minimum cost of the sensor network.…”

Section: Introductionmentioning

confidence: 99%

Sensor network design for maximizing process efficiency: An algorithm and its application

et al. 2014

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in Wiley Online Library (wileyonlinelibrary.com)Sensor network design (SND) is a constrained optimization problem requiring systematic and effective solution algorithms for determining where best to locate sensors. A SND algorithm is developed for maximizing plant efficiency for an estimator-based control system while simultaneously satisfying accuracy requirements for the desired process measurements. The SND problem formulation leads to a mixed integer nonlinear programming (MINLP) optimization that is difficult to solve for large-scale system applications. Therefore, a sequential approach is developed to solve the MINLP problem, where the integer problem for sensor selection is solved using the genetic algorithm while the nonlinear programming problem including convergence of the "tear stream" in the estimator-based control system is solved using the direct substitution method. The SND algorithm is then successfully applied to a large scale, highly integrated chemical process. Figure 3. Algorithm to simulate feedback control system with an estimator. 468

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Section: Introductionmentioning

confidence: 99%

Sensor network design for maximizing process efficiency: An algorithm and its application

et al. 2014

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“…These individual and unrelated treatments usually produce sub-optimal solutions from the plant-wide control point of view. The OSL field generally uses investment costs, observability, Kalman filter theory and dynamic models to define the sensors network by means of integer optimization routines (Musulin et al, 2005;Singh and Hahn, 2005;Kadu et al, 2008;Bhushan et al, 2008). Generally, these strategies are developed on process in open loop or with an already existing control policy.…”

Section: Introductionmentioning

confidence: 99%

Plant-Wide Control Based on Minimum Square Deviation

Zumoffen

Molina

Basualdo

2010

IFAC Proceedings Volumes

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“…The estimator design involves decisions on: (i) the structure (sensor number and locations), and (ii) the kind of (EKF, Luenberger, Geometric, etc) algorithm. In the distillation column estimation field: (i) the EKF has been, by far, the most widely employed and accepted algorithm, (ii) the sensor structure has been decided with heuristics (Oisiovici and Cruz, 2000) or observability measures (van der Berg et al 2000;Singh and Hahn, 2005), (ii) only in a few studies (Alonso et al, 2004;Bian and Henson 2006;Venkateswarlu and Kumar, 2006;Kadu et al, 2008) the measure-based sensor location results have been tested with estimator functioning and is not clear to what extent the results depend on tuning. In principle, the resolution of this drawback requires a unified framework to address the algorithm, sensor location, and tuning aspects.…”

Section: Introductionmentioning

confidence: 99%