scholarly journals Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

Actuators ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 301
Author(s):  
Johannes Ultsch ◽  
Julian Ruggaber ◽  
Andreas Pfeiffer ◽  
Christina Schreppel ◽  
Jakub Tobolář ◽  
...  
Keyword(s):  
Control System ◽  
Embedded System ◽  
Control Systems ◽  
System Modeling ◽  
Differential Algebraic Equations ◽  
Automotive Application ◽  
Model Based ◽  
High Level Modeling ◽  
High Level ◽  
Automated Handling

High-level modeling languages facilitate system modeling and the development of control systems. This is mainly achieved by the automated handling of differential algebraic equations which describe the dynamics of the modeled systems across different physical domains. A wide selection of model libraries provides additional support to the modeling process. Nevertheless, deployment on embedded targets poses a challenge and usually requires manual modification and reimplementation of the control system. The novel proposed eFMI Standard (Functional Mock-up Interface for embedded systems) introduces a workflow and an automated toolchain to simplify the deployment of model-based control systems on embedded targets. This contribution describes the application and verification of the eFMI workflow using a vertical dynamics control problem with an automotive application as an example. The workflow is exemplified by a control system design process which is supported by the a-causal, multi-physical, high-level modeling language Modelica. In this process, the eFMI toolchain is applied to a model-based controller for semi-active dampers and demonstrated using an eFMI-based nonlinear prediction model within a nonlinear Kalman filter. The generated code was successfully tested in different validation steps on the dedicated embedded system. Additionally, tests with a low-volume production electronic control unit (ECU) in a series-produced car demonstrated the correct execution of the controller code under real-world conditions. The novelty of our approach is that it automatically derives an embedded software solution from a high-level multi-physical model with standardized eFMI methodology and tooling. We present one of the first full application scenarios (covering all aspects ranging from multi-physical modeling up to embedded target deployment) of the new eFMI tooling.

From MARTE to Reconfigurable NoCs

2010 ◽  
pp. 135-157
Author(s):  
Imran Rafiq Quadri ◽  
Majdi Elhaji ◽  
Samy Meftali ◽  
Jean-Luc Dekeyser
Keyword(s):  
System Modeling ◽  
Unified Modeling Language ◽  
Modeling And Analysis ◽  
Unified Modeling ◽  
Model Driven ◽  
Abstraction Level ◽  
Management Group ◽  
High Level Modeling ◽  
High Level ◽  
Abstraction Levels

Due to the continuous exponential rise in SoC’s design complexity, there is a critical need to find new seamless methodologies and tools to handle the SoC co-design aspects. We address this issue and propose a novel SoC co-design methodology based on Model Driven Engineering and the MARTE (Modeling and Analysis of Real-Time and Embedded Systems) standard proposed by Object Management Group, to raise the design abstraction levels. Extensions of this standard have enabled us to move from high level specifications to execution platforms such as reconfigurable FPGAs. In this chapter, we present a high level modeling approach that targets modern Network on Chips systems. The overall objective: to perform system modeling at a high abstraction level expressed in Unified Modeling Language (UML); and afterwards, transform these high level models into detailed enriched lower level models in order to automatically generate the necessary code for final FPGA synthesis.

scholarly journals Advice Provision for Energy Saving in Automobile Climate-Control System

AI Magazine ◽  
2015 ◽  
Vol 36 (3) ◽  
pp. 61-72 ◽  
Author(s):  
Amos Azaria ◽  
Ariel Rosenfeld ◽  
Sarit Kraus ◽  
Claudia V. Goldman ◽  
Omer Tsimhoni
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Control Systems ◽  
Control Unit ◽  
Comfort Level ◽  
Environmental Footprint ◽  
Climate Control ◽  
Electric Car ◽  
Save Energy ◽  
High Level

Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. The need to save energy becomes even greater when considering an electric car, since heavy use of the climate control system may exhaust the battery. In this article we consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system’s settings, and the other, models human comfort level as a function of the climate control system’s settings. Using these models, the agent provides advice to the driver considering how to set the climate control system. The agent advises settings which try to preserve a high level of comfort while consuming as little energy as possible. We empirically show that drivers equipped with our agent which provides them with advice significantly save energy as compared to drivers not equipped with our agent.

A SoC for Intelligent Control Systems

2012 ◽  
Vol 571 ◽  
pp. 514-517 ◽  
Author(s):  
Zheng Xing Zheng ◽  
Guo Min Tang ◽  
Li Min Liu
Keyword(s):  
Control System ◽  
Embedded System ◽  
Control Systems ◽  
Intelligent Control ◽  
Industrial Automation ◽  
Intelligent Controller ◽  
Intelligent Control System ◽  
Intelligent Control Systems ◽  
Software And Hardware

Intelligent control is a new direction of industrial automation. An intelligent control system is composed of algorithm, software and hardware. SoC is one of the best embedded system hardware. SoC may get some new progress for intelligent control. In this paper, intelligent control, SoC and some intelligent controller based on SoC are discussed. The new controller can be the smaller and more reliable.

scholarly journals The Design of Robust Controller for Networked Control System with Time Delay

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhongda Lu ◽  
Lijing Wang ◽  
Fengbin Zhang ◽  
Fengxia Xu
Keyword(s):  
Control System ◽  
Time Delay ◽  
Control Systems ◽  
Networked Control Systems ◽  
System Modeling ◽  
Networked Control System ◽  
Networked Control ◽  
Loop Model ◽  
The Stability ◽  
System With Time Delay

This paper considers the stability andH∞control problem of networked control systems with time delay. Taking into account the influence of network with delay, unknown input disturbance, and uncertainties of the system modeling, meanwhile we establish a precise, closed-loop model for networked control systems with time delay. By selecting a proper Lyapunov-Krasovskii function and using Lyapunov theorem, a sufficient condition for stability of the system in the form of LMI is demonstrated, corresponding controller parameters are acquired, and the convergence of the control algorithm is proved. The simulation example shows that the construction of the network robust control system with time delay indeed improves the stability performance of the system, which indicates the effectiveness of the design.

scholarly journals Emergency Incident Management: An Evolving Incident Control System Framework

2009 ◽  
Vol 3 (2) ◽  
pp. 66-75 ◽  
Author(s):  
Ian Dwyer ◽  
Christine Owen
Keyword(s):  
Control System ◽  
Control Systems ◽  
Emergency Services ◽  
Incident Management ◽  
Questionnaire Data ◽  
System Framework ◽  
Management Personnel ◽  
Level Of Satisfaction ◽  
High Level ◽  
In Fire

AbstractThis article reports on an investigation into the use of Incident Control Systems (e.g., AIIMS/ CIMS) by personnel involved in emergency incident management in fire and emergency services agencies in Australia and New Zealand. A questionnaire was distributed that aimed to assess how information flowed between emergency incident management personnel at different layers of the incident control system, and what enabled and constrained coordination between those personnel. Data were collected from personnel on the fire or incident ground; members of Incident Management Teams; as well as staff operating in regional and state centres of coordination. To date there have been 579 responses spread across 24 agencies. The findings reveal that while there is a high level of satisfaction with overall organisational arrangements and reporting relationships, there are some systemic tensions in, and dissatisfaction evident with, communication arrangements. The extent to which Incident Control Systems facilitate the organisational flexibility needed during dynamic and often unpredictable situations is also discussed. Where appropriate, comparisons are made with similar questionnaire data collected in 2003 by AFAC (Australasian Fire Authorities Council).

Model Based Performance Correction Methodology — A Case Study

2014 ◽  
Author(s):  
Koldo Zuniga ◽  
Thomas P. Schmitt ◽  
Herve Clement ◽  
Joao Balaco
Keyword(s):  
Control System ◽  
Control Systems ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Performance Test ◽  
Test Results ◽  
Model Based ◽  
Test Conditions ◽  
Partial Load

Correction curves are of great importance in the performance evaluation of heavy duty gas turbines (HDGT). They provide the means by which to translate performance test results from test conditions to the rated conditions. The correction factors are usually calculated using the original equipment manufacturer (OEM) gas turbine thermal model (a.k.a. cycle deck), varying one parameter at a time throughout a given range of interest. For some parameters bi-variate effects are considered when the associated secondary performance effect of another variable is significant. Although this traditional approach has been widely accepted by the industry, has offered a simple and transparent means of correcting test results, and has provided a reasonably accurate correction methodology for gas turbines with conventional control systems, it neglects the associated interdependence of each correction parameter from the remaining parameters. Also, its inherently static nature is not well suited for today’s modern gas turbine control systems employing integral gas turbine aero-thermal models in the control system that continuously adapt the turbine’s operating parameters to the “as running” aero-thermal component performance characteristics. Accordingly, the most accurate means by which to correct the measured performance from test conditions to the guarantee conditions is by use of Model-Based Performance Corrections, in agreement with the current PTC-22 and ISO 2314, although not commonly used or accepted within the industry. The implementation of Model-based Corrections is presented for the Case Study of a GE 9FA gas turbine upgrade project, with an advanced model-based control system that accommodated a multitude of operating boundaries. Unique plant operating restrictions, coupled with its focus on partial load heat rate, presented a perfect scenario to employ Model-Based Performance Corrections.

scholarly journals Flexible Productive Systems Modeling and Control Tools: Literature Review

2020 ◽  
pp. 91-98
Author(s):  
URC Mazzoni ◽  
OL Asato ◽  
FY Nakamoto
Keyword(s):  
Control System ◽  
Control Systems ◽  
Manufacturing Systems ◽  
New Technologies ◽  
System Modeling ◽  
Production Costs ◽  
Modeling Tools ◽  
Modeling And Control ◽  
Productive Systems ◽  
And Control

The challenges imposed on Manufacturing Systems (MS), given the demands of a dynamic and competitive market, instigates the development of new technologies to promote the reduction of production costs, increase productivity and ensure the level of quality established by the company. Such technologies applied in MS create demands for new paradigms for the design of control systems, mainly about the integration of automated systems, such as multifunction machines, flexible machining centers, intelligent robotic conveyor systems, and the integration of information systems, production planning and management, and manufacturing execution. The main purpose of control system modeling is to represent a real system using conceptual models to visualize, predict and simulate the desired dynamic behavior of the system. This article presents some modeling tools for control systems capable of adequately representing a manufacturing system with all its requirements and intrinsic characteristics, supported by formal methods for structured modeling of the control system.

Sign in / Sign up

Export Citation Format

Share Document