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'Patterns for Time-Triggered Embedded Systems' by Michael J. Pont. pttes_cover_ You can download the complete book (PDF file) here. You can also. In this book, Michael J Pont introduces 70 powerful, proven design techniques ("patterns") for enhancing rapid development and reliability in embedded systems based on the popular microcontroller family. the design & implementation of complete scheduler operating systems for. Patterns for time-triggered embedded systems: Building reliable applications with the family of microcontrollers, by Michael J. Pont (). Addison-Wesley.
Post time: 04 Nov Terms and Conditions: This file may be freely redistributed provided only that this footer remains intact. Excerpts from the Preface: Embedded software is ubiquitous. It forms a core component of an enormous range of systems, from aircraft, passenger cars and medical equipment, to children's toys, video recorders and microwave ovens. This book provides a complete and coherent set of software patterns to support the development of this type of application. What are the key features of this book?
Pont Publisher: English ISBN Book Description In this book, Michael J Pont introduces 70 powerful, proven design techniques "patterns" for enhancing rapid development and reliability in embedded systems based on the popular microcontroller family.
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In control applications, however, time-triggered sampling also called periodic sampling is still dominant. This is mainly because sampled-data control theory in existence basically originates from time-triggered rather than event-triggered sampling. The theory for event-based control is still under development [ 13 , 14 ]. When the resource becomes scarce, however, fixed period based time-triggered sampling will result in worse than possible performance in underloaded conditions and degraded performance even instability in overloaded conditions.
There is an obvious lack of flexibility in time-triggered sampling when the system operates in resource-constrained environments with variable workload. The proposed sampling scheme addresses this problem of time-triggered sampling through using flexible sampling periods at runtime.
The idea of sampling period adaptation is not new, but we will present a new method for adjusting the sampling period, which improves the flexibility and resource efficiency of the system. The rest of this paper is organized as follows. In Section 2, we briefly review related work. Section 3 describes the system model to be considered.
In Section 4 we present the flexible time-triggered sampling scheme, along with the algorithm for sampling period adaptation. Simulations are conducted in Section 5 to evaluate the performance of the proposed scheme in comparison with the conventional non-adaptive time-triggered sampling scheme.
Finally, Section 6 concludes this paper.
Related Work Smart sensors or intelligent sensors have been applied in various engineering systems, for example, [ 15 , 16 ]. A smart sensor is typically composed of several modules, such as sensing unit, AD Analog to Digital converter, microcontroller, storage, transceiver, and power unit. Its capability of data processing enables diverse sampling patterns besides the conventional uniform sampling mechanism. For instance, the concept of send-on-delta, a signal-dependent sampling scheme, has been explored in [ 10 - 12 ] to reduce the number of sensor data transmission.
Willett et al. Almost all of these works have been done for general-purpose signal processing and telecommunication systems in which no control applications are involved. In the literature, relatively little progress has been made on applying event-triggered sampling in control systems.
Otanez et al. Nguyen and Suh [ 18 ] applied the send-on-delta data transmission method in networked control systems achieving improved estimation performance. Despite the increasing interest in this direction, the lack of a unified theory supporting event-based control has been blocking the practical applications of event-triggered sampling methods in control systems. Recently, significant work has been done with sampling period adaptation in resource-constrained real-time control systems that basically use time-triggered sampling.
For instance, Cervin et al. Marti et al. In our previous work [ 21 - 23 ], neural network based and fuzzy logic control based feedback scheduling methods for sampling period adaptation in multitasking control systems have been explored, respectively. An overview of this direction can be found in [ 24 ]. Since the majority of these papers consider CPU resource constraints and are based on utilization control, the relevant methods are generally not suitable for WCSs where the communication resource rather than the computing resource is the major concern and utilization control could potentially be inefficient.
Li and Chow [ 25 ] proposed an adaptive multiple sampling rate scheduling algorithm for Internet-based supervisory control systems. Ploplys et al. Based on the same control structure, Kawka and Alleyne [ 26 ] developed another heuristic algorithm to adapt sampling period with respect to packet loss. Colandairaj et al.
However, none of these papers considers controlling the deadline miss ratio as we do. Consequently, we can address simultaneously the problems of delay and packet loss, whereas almost all existing methods are dedicated to either of them.
In our previous work [ 28 ], we have developed a feedback scheduling method to rescale sampling periods based on deadline miss ratio control for multi-loop networked control systems using priority-based fieldbuses. In contrast, this paper focuses on adaptive sampling in smart sensors used in WCSs. For simplicity, assume each control loop is composed of one smart sensor, one controller, and one smart actuator.
The sensor and the actuator are attached to the controlled process, which is a single-input single-output SISO physical system. All these nodes reside within a collision area in which every pair of nodes can hear from each other, i.
The wireless technology used in the network is ZigBee [ 31 ].