MV bound and MV controller for convex‐non‐linear systems with input constraints

Alipouri, Yousef; Huang, Biao; Kodamana, Hariprasad

doi:10.1049/iet-cta.2017.0760

Cited by 6 publications

(4 citation statements)

References 30 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The statistical characteristics (mean or variance) of the residuals are utilized to evaluate the system control performance and identify the causes of performance degradation. 8,9 The Minimum Variance (MVC) benchmark 10 and the Linear Quadratic Gauss (LQG) benchmark 11 are the most extensively studied performance benchmarks based on mechanism model. In the MVC benchmark, the variance of the output under the minimum variance control is applied as the optimal benchmark.…”

Section: Introductionmentioning

confidence: 99%

A performance assessment methodology with coupling between layers for LP‐DMC systems

Chen,

Li,

Yang

2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

For the estimation of performance improvement potentials of LP‐DMC systems, a novel performance assessment methodology with the optimal benchmark considering the coupling between layers was proposed. The controlled variable tracking residuals and variable fluctuations are introduced as the coupling factors that the dynamic layer acts on the steady‐state layer. Correspondingly, the constraint scaling factor and fluctuation domain are introduced as the coupling factors that the steady‐state layer acts on the dynamic layer. The solutions of the two layers are interactive and iterative and eventually converge to optimal results. Then, the optimal results are utilized as the optimal criteria to define the assessment indexes of LP‐DMC. The experiment results in a simulation and an industrial process indicate the validity and rationality of the proposed methodology.

show abstract

Section: Introductionmentioning

confidence: 99%

A performance assessment methodology with coupling between layers for LP‐DMC systems

Chen,

Li,

Yang

2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…The projection-based recurrent neural network is then utilised to find the optimal solution to the defined cost function. These networks have various structures, and the non-linear projection-based recurrent neural network (PRNN) is one of the most popular RNNs that can deal with the constrained optimization problem [28]. In this paper, this method for the first time has been used for sensor fusion purposes.…”

Section: Introductionmentioning

confidence: 99%

Sensor fusion with high‐order moments constraints using projection‐based neural network

Alipouri

Bonab

Zhong

2021

IET Signal Processing

Self Cite

View full text Add to dashboard Cite

The existing sensor fusion methods mainly follow two approaches, including Gaussian and Non-Gaussian-based sensor fusion approaches. In the first approach, fusion weights are determined based on the second moment. This approach is unable to account for high-order moments; thus, it is not accurate for non-Gaussian sensors. In the second approach, the fusion weights are determined using distribution functions of sensor data. Though this method is more accurate than Gaussian-based sensor fusion, it is a sophisticated method as it requires all moments information of each sensor, which is either not available or at least hard to be identified. Here, we propose an alternative way to determine the fusion weights by a limited number of n (>2) moment information of data. The proposed method makes trades off between accuracy and complexity. The other problem, which has not been studied in the literature, is existence of constraints on moments. The proposed method can address this problem as well. To do this, a projection-based neural network-based optimization method is used to calculate the optimal fusion weights that satisfy moment constraints. A practical application of the proposed sensor fusion method on predicting occupancy for heating, ventilation, and air conditioning (HVAC) is conducted.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

“…Also, various research works extended the MV index for nonlinear systems. [18][19][20] An overview of various indices of performance assessment and their applications and limitations have been described by Jelali 3 and Huang and Shah. 21 As mentioned above, it is necessary to know the time delay of a process to calculate the MV index in SISO systems.…”

Section: Introductionmentioning

confidence: 99%

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Khosroshahi

Poshtan

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

Control system performance assessment is significant, especially in practical applications. One of the most important indices for the performance assessment of control systems is minimum variance. Calculating the minimum variance index in multivariate systems requires prior knowledge of system parameters and models, and is therefore an obstacle in practical applications. In this article, an index is proposed for the performance assessment of multivariate control loops, evaluating the system performance with the minimum variance criterion and using only the system’s routine operation data. This index can quantify the performance using neither any prior knowledge of system parameters nor the system’s optimal operation data. The proposed index is based on the Hurst exponent, a parameter for measuring correlations in time series data. In this article, detrended fluctuation analysis and rescaled range analysis are used to estimate the Hurst exponents of system outputs. Using a combination of these Hurst exponents, an index is defined for the performance assessment of multivariate systems. The results of simulation examples illustrate that the proposed index can assess the performance efficiently.

show abstract

MV bound and MV controller for convex‐non‐linear systems with input constraints

Cited by 6 publications

References 30 publications

A performance assessment methodology with coupling between layers for LP‐DMC systems

A performance assessment methodology with coupling between layers for LP‐DMC systems

Sensor fusion with high‐order moments constraints using projection‐based neural network

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Contact Info

Product

Resources

About