Incorporating Practical Laboratory Experiments to Reinforce Dynamic Systems and Control Concepts

2009 ◽  
Author(s):  
Melody L. Baglione
Keyword(s):  
Dynamic Systems ◽  
Laboratory Experiments ◽  
Pressure Control ◽  
Lessons Learned ◽  
Student Groups ◽  
Modeling And Control ◽  
Faculty Interaction ◽  
Hands On ◽  
Systems And Control ◽  
And Control

The Cooper Union is developing a new simultaneous lecture and laboratory approach to address the pedagogical challenge of finding the appropriate balance between theory and hands-on experimentation in teaching dynamic systems and control concepts. The new approach dedicates one hour each week to laboratory experiments with the class subdivided into small student groups having greater faculty interaction. Bench top experiments from National Instruments and Quanser include DC motor and inverted pendulum modeling and control workstations. Process control test rigs from Feedback Inc. include level, flow, temperature, and pressure control trainers. Devoting significant time to laboratory experiments gives students the opportunities to fully appreciate feedback control concepts and to acquire valuable practical skills. This paper discusses the new instructional approach, preliminary results, lessons learned, and future plans for improving the systems and control curriculum.

A Hands-On Approach in Teaching Dynamic Systems Modeling and Control

2006 ◽  
Author(s):  
Nael Barakat ◽  
Hugh Jack
Keyword(s):  
Dynamic Systems ◽  
Engineering Students ◽  
Financial Burden ◽  
System Control ◽  
Systems Modeling ◽  
Modeling And Control ◽  
Final Project ◽  
Project Description ◽  
Hands On ◽  
And Control

Most engineering products nowadays are multi-part integrated systems that are produced by teams of engineers. These systems are characterized by their complexity and diversity of components that range between being fully mechanical to being fully electrical components. A vital aspect in successfully building and running of these systems is the proper modeling and control of their dynamics. As mechanical engineering students graduate and face this reality, a hands-on preparation to deal with similar systems during college experience becomes very rewarding. The important elements of applying knowledge in dynamic systems modeling and control are practiced during the laboratory session in college. At the Grand Valley State University (GVSU) School of Engineering (SOE) the integration of electrical, mechanical and software systems is instructed and practiced in a required course (EGR 345) entitled "Dynamic systems Modeling and Control." This course includes a theoretical part where principles of system dynamics, system components, and system control are emphasized. The course capitalizes on students' previous knowledge of the simple isolated systems and modifies their strategies and approach to look and treat engineering systems as complete integrated entities. In addition, the course includes a significant lab component and a major project through which the student gains vital hands-on experience. In this paper, the philosophy and major components of the course are discussed. The focus is on presenting a sequence of lab experiments that serve the application of principles of dynamic systems modeling and control, as well as the final project. These experiments are characterized by its comprehensiveness and cost effectiveness. Moreover, an innovative method of making the lab equipment available to the students, and mostly owned by them, will also be summarized. As this approach minimizes the financial burden of the lab equipment, it also gives the students an element of ownership and comfort dealing with equipment they own and use. As a matter of fact, it ultimately leads to the utilization of these pieces of equipment in an innovative way to produce an engineering electromechanical system that will perform the tasks required by their final project description. A discussion on the pros and cons in the outcomes of this approach and some modification plans for the next course offering will be provided at the end of the paper.

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.

Modeling and Control of SMT Manufacturing Lines Using Hybrid Dynamic Systems

2003 ◽  
pp. 66-80 ◽  
Author(s):  
L. G. Barajas ◽  
A. Kansal ◽  
A. Saxena ◽  
M. Egerstedt ◽  
A. Goldstein ◽  
...  
Keyword(s):  
Dynamic Systems ◽  
Modeling And Control ◽  
And Control

Dynamic Modeling and Control of Packing Pressure in Injection Molding

1994 ◽  
Vol 116 (2) ◽  
pp. 244-249 ◽  
Author(s):  
J. Hu ◽  
J. H. Vogel
Keyword(s):  
Injection Molding ◽  
Closed Loop ◽  
Good Control ◽  
Pressure Control ◽  
Physical Parameters ◽  
Closed Loop System ◽  
Modeling And Control ◽  
Packing Pressure ◽  
And Control ◽  
Plastic Injection

A dynamic model of injection molding developed from physical considerations is used to select PID gains for pressure control during the packing phase of thermo-plastic injection molding. The relative importance of various aspects of the model and values for particular physical parameters were identified experimentally. The controller gains were chosen by pole-zero cancellation and root-locus methods, resulting in good control performance. Both open and closed-loop system responses were predicted and verified, with good overall agreement.

Dynamic Systems and Control

2021 ◽  
pp. 403-475
Author(s):  
Kostas Triantafyllopoulos
Keyword(s):  
Dynamic Systems ◽  
Systems And Control ◽  
And Control

Modeling and Control of Interconnected Dimensionless Dynamic Systems

2009 ◽  
Author(s):  
Scott Manwaring ◽  
Andrew Alleyne
Keyword(s):  
Dimensional Analysis ◽  
Dynamic Systems ◽  
Controller Design ◽  
Fluid Power ◽  
Modeling And Control ◽  
Initial Investigation ◽  
And Control ◽  
Insight Into ◽  
Original Dynamic

Previous work has found benefit in using dimensional analysis in the modeling and control of dynamic systems. What has not been explored is how multiple dimensionless dynamic systems would interconnect and interact with one another. This work presents an initial investigation into the interconnection of dimensionless dynamic systems, including an analysis of the differences between interconnecting dimensioned and dimensionless systems. A strategy is developed to interconnect dimensionless dynamic systems and explored using models of multiple fluid power components. The interconnection strategy is tested through controller design and simulation, which reveals insight into the dimensionless transformation of the original dynamic systems.

Sign in / Sign up

Export Citation Format

Share Document