Performance Analysis of Integrated Communication and Control System Networks

1990 ◽  
Vol 112 (3) ◽  
pp. 365-371 ◽  
Author(s):  
Y. Halevi ◽  
A. Ray
Keyword(s):  
Control System ◽  
Statistical Analysis ◽  
Performance Analysis ◽  
Control Systems ◽  
Manufacturing Plants ◽  
Asynchronous Time Division Multiplexing ◽  
Processing Plants ◽  
Integrated Communication ◽  
Time Division ◽  
And Control

This paper presents statistical analysis of delays in Integrated Communication and Control System (ICCS) networks [1–4] that are based on asynchronous time-division multiplexing. The models are obtained in closed form for analyzing control systems with randomly varying delays. The results of this research are applicable to ICCS design for complex dynamical processes like advanced aircraft and spacecraft, autonomous manufacturing plants, and chemical and processing plants.

On Modeling of Integrated Communication and Control Systems

1990 ◽  
Vol 112 (4) ◽  
pp. 790-794 ◽  
Author(s):  
Luen-Woei Liou ◽  
Asok Ray
Keyword(s):  
Communication Network ◽  
Control System ◽  
Control Systems ◽  
Distributed Delays ◽  
Asynchronous Time Division Multiplexing ◽  
Integrated Communication ◽  
Time Division ◽  
And Control ◽  
Relationship Of ◽  
The Relationship

In a two-part paper [1,2], Ray and Halevi reported modeling of Integrated Communication and Control Systems (ICCS). Varying and distributed delays are introduced in the control system due to asynchronous time-division multiplexing in the communication network. This correspondence illustrates the relationship of Ray and Halevi’s approach to that of Kalman and Bertram [3] under nonsynchronous sampling.

Integrated Communication and Control Systems: Part I—Analysis

1988 ◽  
Vol 110 (4) ◽  
pp. 367-373 ◽  
Author(s):  
Yoram Halevi ◽  
Asok Ray
Keyword(s):  
Control System ◽  
Control Systems ◽  
Dynamic Performance ◽  
Digital Data ◽  
Time Varying ◽  
Data Traffic ◽  
Time Model ◽  
Loop Control ◽  
Integrated Communication ◽  
And Control

Computer networking is a reliable and efficient means for communications between disparate and distributed components in complex dynamical processes like advanced aircraft, spacecraft, and autonomous manufacturing plants. The role of Integrated Communication and Control Systems (ICCS) is to coordinate and perform interrelated functions, ranging from real-time multi-loop control to information display and routine maintenance support. In ICCS, a feedback control loop is closed via the common communication channel which multiplexes digital data from the sensor to the controller and from the controller to the actuator along with the data traffic from other loops and management functions. Due to the asynchronous time-division multiplexing of the network protocol, time-varying and possibly stochastic delays are introduced in the control system, which degrade the system dynamic performance and are a source of potential instability. The paper is divided into two parts. In the first part, the delayed control system is represented by a finite-dimensional, time-varying, discrete-time model which is less complex than the existing continuous-time models for time-varying delays; this approach allows for simpler schemes for analysis and simulation of ICCS. The second part of the paper addresses ICCS design considerations and presents simulation results for certain operational scenarios of ICCS.

Integrated Communication and Control Systems: Part III—Nonidentical Sensor and Controller Sampling

1990 ◽  
Vol 112 (3) ◽  
pp. 357-364 ◽  
Author(s):  
Luen-Woei Liou ◽  
A. Ray
Keyword(s):  
Control System ◽  
Control Systems ◽  
Dynamic Performance ◽  
Time Dependent ◽  
Dimensional Model ◽  
Analysis And Design ◽  
Finite Dimensional ◽  
Alternative Approaches ◽  
Integrated Communication ◽  
And Control

Networking in Integrated Communication and Control Systems (ICCS) introduces randomly varying delays which degrade the system dynamic performance and are a source of potential instability. In Part I [1] of this sequence of papers we developed a discrete-time, finite-dimensional model of the delayed control system for analysis and design of ICCS where the sensor and controller have identical sampling rates. In Part II [2] we proposed two alternative approaches for ICCS design, namely, identical and nonidentical sampling rates for sensor and controller. This Part III presents extended modeling of ICCS for nonidentical sensor and controller sampling rates. This model is also suitable for analyzing tracking problems, i.e., control systems with time-dependent reference inputs.

Reliability analysis and safety model checking of Safety-Critical and control Systems: A case study of NPP control system

2022 ◽  
Vol 166 ◽  
pp. 108812
Author(s):  
Vinay Kumar ◽  
Kailash Chandra Mishra ◽  
Pooja Singh ◽  
Aditya Narayan Hati ◽  
Mohan Rao Mamdikar ◽  
...  
Keyword(s):  
Control System ◽  
Model Checking ◽  
Reliability Analysis ◽  
Control Systems ◽  
Safety Critical ◽  
And Control

Development of Instrumentation and Control Systems for UK ABWR

2017 ◽  
Author(s):  
Itsuki Naito ◽  
Taisuke Koyamada ◽  
Keisuke Yamamoto ◽  
Kingo Igarashi ◽  
Hideo Harada ◽  
...  
Keyword(s):  
Control System ◽  
Control Systems ◽  
Good Practice ◽  
Operating Experience ◽  
Severe Accident ◽  
Regulatory Regime ◽  
Design Assessment ◽  
Class 1 ◽  
The Uk ◽  
And Control

This paper introduces the Instrumentation and Control (I&C) system for the proposed UK Advanced Boiling Water Reactor (UK ABWR) offered by Hitachi-GE Nuclear Energy, Ltd (Hitachi-GE). Hitachi-GE has been progressing the UK Generic Design Assessment (GDA) licensing process over the last 3 years. This is the process through which the Office for Nuclear Regulations (ONR) assesses the UK ABWR for suitability from a nuclear safety, security, environmental protection and waste management perspective and it is the first step towards proceeding with the construction phase in the UK. ONR’s regulatory expectations setting out relevant good practice are described in the Safety Assessment principles (SAPs), which are considered into the I&C design for UK ABWR. In addition, it has also been designed to take into account relevant good practices and regulations. In accordance with expectations derived from SAPs, the UK ABWR I&C systems are categorized and classified as required by IEC 61513 and IEC 61226. In addition, the overall I&C architecture, including all associated Human-Machine Interfaces (HMIs), abides by the principles independence and diversity of safety measures, segregation and separation of the protection and control systems. As a result, the UK ABWR I&C architecture is composed of major eight sub-systems. The eight sub-systems are: -Safety System Logic and Control system (SSLC) -Hardwired Backup System (HWBS) -Safety Auxiliary Control System (SACS) -Plant Control System (PCntlS) -Reactor/Turbine Auxiliary Control System (RTACS) -Plant Computer System (PCS) -Severe Accident Control and Instrumentation system (SA C&I) -Other dedicated C&I systems. The features for each sub-system such as redundancy of safety train or segregation among divisions are specified so that each sub-system will achieve its reliability as well as increase availability. While in the Japanese ABWR safety I&C system, the main protection system (SSLC), is microprocessor-based from the decades of successful operating experience in the past BWR, to meet the UK regulatory regime expectation on diversity between Class 1 platform and non-Class 1 platform, the SSLC (Class 1) for the UK ABWR is by Field Programmable Gate Array (FPGA). This system is currently under development and complies with IEC 62556. Its safety integrity level is planned to be SIL 3 (as a single division) and SIL 4 (as a four division system) as defined in IEC 61508. The HMIs which constitute an integral part of the I&C systems are also designed to comply with the I&C architecture regarding their categorization and classification with consideration of Human Factors (HF) modern methods taken into accounts.

scholarly journals PROPOSAL FOR NAVIGATION AND CONTROL SYSTEM FOR SMALL UAV

Aviation ◽  
2010 ◽  
Vol 14 (3) ◽  
pp. 77-82 ◽  
Author(s):  
Grzegorz Kopecki ◽  
Jacek Pieniążek ◽  
Tomasz Rogalski ◽  
Pawel Rzucidło ◽  
Andrzej Tomczyk
Keyword(s):  
Control System ◽  
Control Systems ◽  
Human Factor ◽  
General Structure ◽  
Failure Detection ◽  
System Reconfiguration ◽  
University Of Technology ◽  
Small Uav ◽  
Navigation And Control ◽  
And Control

The article presents the project of UAV control system realized at Department of Avionics and Control Systems of Rzeszów University of Technology. The project is based on earlier experiences. In the article general structure of the onboard control system is shown as well as the structure of control station. There are described in proposed control and navigation procedures as well as human factor, failure detection and system reconfiguration. Santrauka Šiame straipsnyje aprašomas bepiločiu orlaiviu kontroles sistemos projekto lgyvendinimas Ržešovo technologijos universiteto Aviadjos prietaisu ir kontroles sistemu katedroje. Projektas atliktas remiantis ankstesne patirtimi. Pateikta ne tik borto sistemu bendroji struktūra, bet ir kontroles stočiu struktūra. Darbe nagrinejamas žmogaus veiksnys, gedimu aptikimas ir sistemu rekonfigūravimas pasiūlytose kontroles ir navigacijos procedūrose.

A Stochastic Regulator for Integrated Communication and Control Systems: Part II—Numerical Analysis and Simulation

1991 ◽  
Vol 113 (4) ◽  
pp. 612-619 ◽  
Author(s):  
Luen-Woei Liou ◽  
Asok Ray
Keyword(s):  
Control Systems ◽  
Numerical Procedure ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Control Law ◽  
Linear Quadratic ◽  
Simulation Experiments ◽  
Integrated Communication ◽  
And Control ◽  
Future Work

A state feedback control law has been derived in Part I [1] of this two-part paper on the basis of an augmented plant model [2, 3, 4] that accounts for the randomly varying delays induced by the network in Integrated Communication and Control Systems (ICCS). The control algorithm was formulated as a linear quadratic regulator problem and then solved using the principle of dynamic programming and optimality. This paper, which is the second of two parts, presents (i) a numerical procedure for synthesizing the control parameters and (ii) results of simulation experiments for verification of the above control law using the flight dynamic model of an advanced aircraft. This two-part paper is concluded with recommendations for future work.

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