Graphoid-based methodologies in modeling, analysis, identification and control of networks of dynamic systems

2016 ◽  
Author(s):  
Donatello Materassi ◽  
Murti V. Salapaka
Keyword(s):  
Dynamic Systems ◽  
Modeling Analysis ◽  
And Control ◽  
Control Of Networks

Erratum: “Modeling the Gas Exchange Processes of a Modern Diesel Engine With an Integrated Physics-Based and Data-Driven Approach” [ASME 2019 Dynamic Systems and Control Conference, Volume 2: Modeling and Control of Engine and Aftertreatment Systems; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Validation; Motion Planning and Tracking Control; Multi-Agent and Networked Systems; Renewable and Smart Energy Systems; Thermal Energy Systems; Uncertain Systems and Robustness; Unmanned Ground and Aerial Vehicles; Vehicle Dynamics and Stability; Vibrations: Modeling, Analysis, and Control, Park City, Utah, USA, October 8–11, 2019, Conference Sponsors: Dynamic Systems and Control Division, ISBN: 978-0-7918-5915-5, Copyright © 2019 by ASME. Paper No. DSCC2019-9226, pp. V002T11A004; 10 pages; doi: 10.1115/DSCC2019-9226]

2019 ◽  
Author(s):  
Jorge Pulpeiro Gonzalez ◽  
King Ankobea-Ansah ◽  
Elena Escuder Milian ◽  
Carrie M. Hall
Keyword(s):  
Dynamic Systems ◽  
Energy Systems ◽  
Systems Modeling ◽  
Modeling And Control ◽  
Modeling Analysis ◽  
Smart Energy Systems ◽  
Systems And Control ◽  
Data Driven Approach ◽  
And Control ◽  
Aftertreatment Systems

Abstract This erratum corrects errors that appeared in the paper “Modeling the Gas Exchange Processes of a Modern Diesel Engine With an Integrated Physics-Based and Data-Driven Approach” which was published in Proceedings of the ASME 2019 Dynamic Systems and Control Conference, Volume 2: Modeling and Control of Engine and Aftertreatment Systems; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Validation; Motion Planning and Tracking Control; Multi-Agent and Networked Systems; Renewable and Smart Energy Systems; Thermal Energy Systems; Uncertain Systems and Robustness; Unmanned Ground and Aerial Vehicles; Vehicle Dynamics and Stability; Vibrations: Modeling, Analysis, and Control, (V002T11A004), October 2019, DSCC2019-9226, doi: 10.1115/DSCC2019-9226.

NEURAL MODELING AND CONTROL OF DYNAMIC SYSTEMS WITH HYSTERESIS

2007 ◽  
Vol 31 (1) ◽  
pp. 127-141
Author(s):  
Yonghong Tan ◽  
Xinlong Zhao
Keyword(s):  
Dynamic Systems ◽  
Control Strategy ◽  
Neural Modeling ◽  
Modeling And Control ◽  
Control Scheme ◽  
The Neural Networks ◽  
Adaptive Control Strategy ◽  
Set Up ◽  
Modeling Strategy ◽  
And Control

A hysteretic operator is proposed to set up an expanded input space so as to transform the multi-valued mapping of hysteresis to a one-to-one mapping so that the neural networks can be applied to model of the behavior of hysteresis. Based on the proposed neural modeling strategy for hysteresis, a pseudo control scheme is developed to handle the control of nonlinear dynamic systems with hysteresis. A neural estimator is constructed to predict the system residual so that it avoids constructing the inverse model of hysteresis. Thus, the control strategy can be used for the case where the output of hysteresis is unmeasurable directly. Then, the corresponding adaptive control strategy is presented. The application of the novel modeling approach to hysteresis in a piezoelectric actuator is illustrated. Then a numerical example of using the proposed control strategy for a nonlinear system with hysteresis is presented.

Sign in / Sign up

Export Citation Format

Share Document