Self - Organizing Migrating Algorithm in Model Predictive Control: Case study on Semi-Batch Chemical Reactor

Macků, Lubomír; Samek, David

doi:10.2507/26th.daaam.proceedings.033

DAAAM Proceedings

2016

DOI: 10.2507/26th.daaam.proceedings.033

|View full text |Cite

Self - Organizing Migrating Algorithm in Model Predictive Control: Case study on Semi-Batch Chemical Reactor

Lubomír Macků¹,

David Samek²

Abstract: Control of complex nonlinear systems brings challenges in the controller design. One of methods how to cope with this challenge is the usage of advanced optimization methods. This work presents application of self-organizing migrating algorithm (SOMA) in control of the semi-batch reactor. The reactor is used in chromium recycling process in leather industry. Because of the complexity of this semi-batch reactor control, the model predictive control (MPC) approach is used. The MPC controller includes self-organi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Tracking System for Moving Object with Forecasting

Kobzev¹,

Novikova²,

Lekareva³

2017

DAAAM Proceedings

View full text Add to dashboard Cite

This Publication has to be referred as: Kobzev AbstractA characteristic feature of tracking systems for movable objects is the change of trajectory according to various laws. Under the uncertainty of these trajectories, the question of ensuring of a given tracking accuracy is topical. One of the possible options for constructing a rational structure of the automatic control system that enhances the control characteristics is the introduction of a parallel model that generates a signal for «additional control». Hence, it is possible to use two structures of a parallel model: 1) a model similar to the original ACS, but with the main feedback on the adjustable coordinate; 2) a model that forms a given («reference») transient process under various external influences. Studies have shown that the introduction of a parallel model having a structure similar to the original ACS, but with a control object completely covered by feedback, allows to stabilize the response characteristics only in a limited range of external influences. ACSs with a parallel model of the second type have higher response performance. Therefore, both in the first and in the second case it is necessary to introduce an element with a variable transmission coefficient into the circuit of "additional control" signal synthesis, which significantly complicates the structure of the automatic control system and the control problem, since its value depends on many factors. The introduction of the predictive model allows to improve the response characteristics of the system significantly. The results of the ACS simulation with the parallel predictive model indicate that the maximum absolute error of the controlled quantity does not exceed 1% of the maximum value of the control input.Keywords: Dynamic objects with the incompletely observed regulated coordinate; predictive control; pattern model; correction algorithms of the controlling action; forecasting of the external effects

show abstract

Tracking System for Moving Object with Forecasting

Kobzev¹,

Novikova²,

Lekareva³

2017

DAAAM Proceedings

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Self - Organizing Migrating Algorithm in Model Predictive Control: Case study on Semi-Batch Chemical Reactor

Cited by 1 publication

References 23 publications

Tracking System for Moving Object with Forecasting

Tracking System for Moving Object with Forecasting

Contact Info

Product

Resources

About