Novel Multiagent Model-Predictive Control Performance Indices for Monitoring of a Large-Scale Distributed Water System

Kordestani, Mojtaba; Safavi, Ali Akbar; Sharafi, Narjes; Saif, Mehrdad

doi:10.1109/jsyst.2016.2628774

Cited by 24 publications

(7 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The above assumption is valid in practical digital processing of the observer since the sampling period of the observer is short enough compared with the variation of ρ. If Equation (20) with I a = 0, then Equation (28) by using Equations ( 29) and ( 30) can be represented by . It is a negative semi-definite for .…”

Section: Second Layermentioning

confidence: 99%

“…Besides, some performance monitoring methods [18][19][20][21] to enhance evaluations of performance indexes were proposed. Liu et al [18] proposed the user-specified benchmark to control loop performance assessment, in which a linear matrix inequality-based approach and a cone complementarity linearization algorithm is applied to find an optimal solution.…”

Section: Introductionmentioning

confidence: 99%

“…Cano-Izquierdo et al [19] proposed a new neuro-fuzzy architecture method for monitoring and performance assessment that allows for a self-organizing classification of dynamic signals and building categories. Kordestani et al [20] proposed a multiagent predictive control with control performance assessment (CPA) for controlling the subsystems in the large-scale systems (LSSs) that are the Rhine-Meuse Delta water system in the Netherlands to analyze the efficiency of controllers in the LSSs. Kordestani et al [21] proposed a new fault tolerant control (FTC) methodology for preventing floods in the land areas close to the Rhine-Meuse Delta water system in the Netherlands.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Chen

Shih

et al. 2020

Mathematics

View full text Add to dashboard Cite

Due to a good ability of learning for nonlinear uncertainties, a mixed modified recurring Rogers-Szego polynomials neural network (MMRRSPNN) control with mended grey wolf optimization (MGWO) by using two linear adjusted factors is proposed to the six-phase induction motor (SIM) expelling continuously variable transmission (CVT) organized system for acquiring better control performance. The control system can execute MRRSPNN control with a fitted learning rule, and repay control with an evaluated rule. In the light of the Lyapunov stability theorem, the fitted learning rule in the MRRSPNN control can be derived, and the evaluated rule of the repay control can be originated. Besides, the MGWO by using two linear adjusted factors yields two changeable learning rates for two parameters to find two ideal values and to speed-up convergence of weights. Experimental results in comparisons with some control systems are demonstrated to confirm that the proposed control system can achieve better control performance.

show abstract

Section: Second Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Chen

Shih

et al. 2020

Mathematics

View full text Add to dashboard Cite

show abstract

“…To improve the reliability, flexibility, and scalability of dc microgrids, many distributed control algorithms, i.e., each DG's controller only communicates with its neighbors', are developed for dc microgrids over the past decade [16]- [19]. These algorithms are usually designed based on consensus techniques [20]- [23] to realize proportional load sharing and global average bus voltage regulation [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

Operation Loss Minimization Targeted Distributed Optimal Control of DC Microgrids

Fan

Peng

et al. 2021

IEEE Systems Journal

View full text Add to dashboard Cite

“…Huang achieved the performance diagnosis of MPC by using the Bayesian probability framework [37]. Kordestani determined the root causes of performance degradation through cross-correlation analysis [38]. Separation of variables is suitable for the analysis of measurable variables, which may lead the controller performance to change, but it is difficult to judge the root cause of the controller performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Performance Monitoring for Model Predictive Control Using a mahalanobis distance based overall index

Zhang

et al. 2018

Asian Journal of Control

View full text Add to dashboard Cite

This paper proposes a data-driven approach for model predictive control (MPC) performance monitoring. It explores the I/O data of the MPC system. First, to evaluate the MPC performance and capture the fluctuation of the process variables, we present an overall performance index based on Mahalanobis distance (MDBI) with its deduced benchmark. The Mahalanobis distance can better characterize the change of the process variable in both principal component space and residual space. As the proper vectors of the two spaces are orthogonal, the MDBI eliminates the correlation between the process variables while considering the variables' characteristics in both spaces simultaneously, which helps evaluate the MPC performance more effectively with fewer monitoring parameters. Furthermore, for the MPC performance diagnosis, we use the MDBI as inputs and construct a support vector machine (SVM) pattern classifier. The classifier can achieve a higher accuracy when recognizing four common performance degradation patterns and determine the root cause of performance degradation. The results of simulations on the Wood-Berry distillation column process and experiments on NIAT multifunctional experiment platform illustrate the effectiveness of the proposed performance assessment/diagnosis strategies.

show abstract

Novel Multiagent Model-Predictive Control Performance Indices for Monitoring of a Large-Scale Distributed Water System

Cited by 24 publications

References 42 publications

Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Operation Loss Minimization Targeted Distributed Optimal Control of DC Microgrids

Data‐Driven Performance Monitoring for Model Predictive Control Using a mahalanobis distance based overall index

Contact Info

Product

Resources

About