scholarly journals Message Scheduling Algorithm Design for Switched FlexRay Network

2017 ◽  
Vol 7 (2) ◽  
pp. 109-115
Author(s):  
Yi-Nan Xu ◽  
Keyword(s):  
Scheduling Algorithm ◽  
Algorithm Design ◽  
Message Scheduling

scholarly journals Scheduling computations with provably low synchronization overheads

2021 ◽  
Author(s):  
Guilherme Rito ◽  
Hervé Paulino
Keyword(s):  
Great Improvement ◽  
Execution Time ◽  
Path Length ◽  
State Of The Art ◽  
Critical Path ◽  
Scheduling Algorithm ◽  
Algorithm Design ◽  
Work Stealing ◽  
Maximum Cost

Abstract We present a Work Stealing scheduling algorithm that provably avoids most synchronization overheads by keeping processors’ deques entirely private by default and only exposing work when requested by thieves. This is the first paper that obtains bounds on the synchronization overheads that are (essentially) independent of the total amount of work, thus corresponding to a great improvement, in both algorithm design and theory, over state-of-the-art Work Stealing algorithms. Consider any computation with work T1 and critical-path length T1 executed by P processors using our scheduler. Our analysis shows that the expected execution time is O T1 P + T1 , and the expected synchronization overheads incurred during the execution are at most O ((CCAS + CMF ence) P T1), where CCAS and CMF ence respectively denote the maximum cost of executing a Compare-And-Swap instruction and a Memory Fence instruction.

Scheduling Algorithm Design for Enhanced Scheduling Fairness

2011 ◽  
Vol 30 (6) ◽  
pp. 1462-1465
Author(s):  
Bin-yang Xu ◽  
He-ping Pu ◽  
Shao-qian Li
Keyword(s):  
Scheduling Algorithm ◽  
Algorithm Design

scholarly journals A FlexRay Dynamic Segment Message Scheduling Optimization Algorithm*

2018 ◽  
Vol 232 ◽  
pp. 03035
Author(s):  
Liangyu Zhang ◽  
Yi Wang
Keyword(s):  
Response Time ◽  
Collaborative Design ◽  
Scheduling Algorithm ◽  
Matlab Simulation ◽  
Worst Case ◽  
Message Scheduling ◽  
Heuristic Scheduling ◽  
Scheduling Method ◽  
Scheduling Optimization Algorithm ◽  
Case Response

In this paper, we present a new heuristic scheduling algorithm basing on DM algorithm, which makes up for the deficiency of the currently very little collaborative design of FlexRay dynamic segment scheduling method with the worst response time and bus utilization. The algorithm improves the comprehensive efficiency of message worst-case response time and uses bus utilization as the optimization target, and the best FID allocation scheme is obtained. The MATLAB simulation experiment of the scheduling scheme obtained good simulation results.

scholarly journals Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Najah Yousfi Allagui ◽  
Farhan A. Salem ◽  
Awad M. Aljuaid
Keyword(s):  
Mobile Robot ◽  
Control Algorithm ◽  
Scheduling Algorithm ◽  
Algorithm Design ◽  
System Response ◽  
System Control ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Differential Drive ◽  
Proportional Integral

Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

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