Expert-aided Control Engineering Environment for Nonlinear Systems

1987 ◽  
Vol 20 (5) ◽  
pp. 369-374 ◽  
Author(s):  
J.H. Taylor ◽  
J.R. James ◽  
D.K. Frederick
Keyword(s):  
Nonlinear Systems ◽  
Control Engineering ◽  
Engineering Environment

scholarly journals UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

2021 ◽  
Author(s):  
Dennis Wolf ◽  
Andreas Engel ◽  
Tajas Ruschke ◽  
Andreas Koch ◽  
Christian Hochberger
Keyword(s):  
Computing System ◽  
Coarse Grained ◽  
Instruction Level Parallelism ◽  
Control Engineering ◽  
Processing Elements ◽  
Actual Application ◽  
Reconfigurable Arrays ◽  
Engineering Environment ◽  
On Chip ◽  
Level Parallelism

AbstractCoarse Grained Reconfigurable Arrays (CGRAs) or Architectures are a concept for hardware accelerators based on the idea of distributing workload over Processing Elements. These processors exploit instruction level parallelism, while being energy efficient due to their simplistic internal structure. However, the incorporation into a complete computing system raises severe challenges at the hardware and software level. This article evaluates a CGRA integrated into a control engineering environment targeting a Xilinx Zynq System on Chip (SoC) in detail. Besides the actual application execution performance, the practicability of the configuration toolchain is validated. Challenges of the real-world integration are discussed and practical insights are highlighted.

scholarly journals Nonlinear system identification using heterogeneous multiple models

2013 ◽  
Vol 23 (1) ◽  
pp. 103-115 ◽  
Author(s):  
Rodolfo Orjuela ◽  
Benoît Marx ◽  
José Ragot ◽  
Didier Maquin
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Nonlinear System Identification ◽  
Multiple Models ◽  
Special Focus ◽  
Multiple Model ◽  
Control Engineering ◽  
State Spaces ◽  
Output Tracking ◽  
Engineering Problems

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to that of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with off-line identification of nonlinear systems based on heterogeneous multiple models. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called the no output tracking effect. The origin of this difficulty is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of the model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

A Platform Neutral Computer Aided Control Engineering Environment

1997 ◽  
Vol 30 (4) ◽  
pp. 79-84
Author(s):  
Al Sadiq A.M.M Halepota ◽  
Philip W. Grant ◽  
Christopher P. Jobling
Keyword(s):  
Control Engineering ◽  
Computer Aided ◽  
Engineering Environment

scholarly journals Multi-Agent Systems in Control Engineering: A Survey

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Fatemeh Daneshfar ◽  
Hassan Bevrani
Keyword(s):  
Control Systems ◽  
Control Design ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Control Engineering ◽  
Agent Based ◽  
Agent Systems ◽  
Design And Implementation ◽  
Engineering Environment ◽  
Multi Agent

This paper presents a survey on multi-agent system (MAS) capabilities in control engineering applications. It describes essential concepts of multi-agent systems that are related to the control systems and presents an overview on the most important control engineering issues which MAS can be explored. Most important technical aspects in MAS implementation and development in engineering environment are also explained. Design methodologies, standards, tools, and supporting technologies to provide an effective MAS-based control design are addressed and a discussion on important related standards and protocols is given. Finally, some comments and new perspectives for design and implementation of agent-based control systems are presented.

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