A COMPARATIVE STUDY OF DIFFERENT OPTIMIZATION TECHNIQUES IN MODELLING AND PREDICTIVE CONTROLS

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study summarizes different optimization techniques in modeling and predictive controls. Optimization plays an important role in Model Predictive Control (MPC). The optimization techniques help to evaluate the best solution to a mathematical model by comparing it with visual data. The different optimization techniques include the Polak method, Cauchy method, Newton method, Modified Newton method, Steep Decent method, David-Fletcher-Powell (DFP) method, and Momentum method. These optimization techniques can fit linear and non-linear models with n-number input variables. This can help to improve the performance of the MPC controller by addressing the challenges of the data being controlled. The solution to this problem is addressed in this study, including the mathematical modeling of optimization techniques. Simulation is conducted to verify the comparison test and model a nonlinear dynamic process. The output of the MPC controller shows a linear agreement with the open-loop test, for all the optimization techniques, with some deviation in the overshoot, rise times and settling times.

Original languageEnglish (US)
Title of host publicationDynamics, Vibration, and Control
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791887639
DOIs
StatePublished - 2023
EventASME 2023 International Mechanical Engineering Congress and Exposition, IMECE 2023 - New Orleans, United States
Duration: Oct 29 2023Nov 2 2023

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume6

Conference

ConferenceASME 2023 International Mechanical Engineering Congress and Exposition, IMECE 2023
Country/TerritoryUnited States
CityNew Orleans
Period10/29/2311/2/23

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

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