By Christophe Aubrun, Daniel Simon, Ye-Qiong Song
This booklet describes co-design ways, and establishes the hyperlinks among the QoC (Quality of keep an eye on) and QoS (Quality of provider) of the community and computing assets. The tools and instruments defined during this booklet keep in mind, at layout point, a number of parameters and houses that needs to be happy by means of structures managed via a community. one of the vital community houses tested are the QoC, the dependability of the approach, and the feasibility of the real-time scheduling of projects and messages. right exploitation of those ways allows effective layout, prognosis, and implementation of the NCS. This e-book can be of serious curiosity to researchers and complicated scholars in computerized keep watch over, real-time computing, and networking domain names, and to engineers tasked with improvement of NCS, in addition to these operating in similar community layout and engineering fields.
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Additional info for Co-design Approaches to Dependable Networked Control Systems
1. info Preliminary Notions 25 to transit to another state. If there is more than 1 missed deadline, the system is in a dynamic failure state. Otherwise, the system is in a normal state. For the efﬁcient overload management, we adopt dropping strategy: any instance that cannot be executed before its deadline is dropped. So whenever talking about (m, k)-ﬁrm in this book, a missed deadline is equivalent to an instance drop. If a control system can accept control performance degradation until k − m deadlines misses (or equivalent packet losses) among any k consecutive ones, the system can then be designed according to the (m, k)-ﬁrm approach to offer the variable levels of control performance between (k, k)-ﬁrm (ideal case) and (m, k)-ﬁrm (worst case) with as many intermediate levels as the possible values there are between k and m.
Hardware in the loop simulation: periods and load off-the-shelf RTOS, which anyway must be instrumented with a task-execution-time operator1. In this application, the period of the feedback scheduler has been ﬁxed to 30 ms to be larger than the robot control tasks (whose limits have been set here from 1 ms to 30 ms). In this experiment, due to the poor quality of the cost functions which were identiﬁed, the feedback scheduler directly controls the CPU usage rather than taking into account the state of the physical system as in an ideal case.
Hence a selective data drop policy (as in [JIA 07]) or a computing power allocation to selected tasks (as in [BEN 06]) can be used to perform optimal control of a plant under constraint of computing or communication limitations. This latter approach is well suited for non pre-emptive scheduling of control tasks and for networked control systems subject to message loss: the tasks or messages are scheduled to jointly perform congestion avoidance and optimal control. t. the implementation induced timing uncertainties, allows for the design and implementation of real-time control systems based on their average execution behavior rather than on pessimistic worst-case estimates.