Neural Feedback Scheduling of Real-Time Control Tasks
Xia, Feng, Tian, Yu-Chu, Sun, Youxian, & Dong, Jinxiang (2008) Neural Feedback Scheduling of Real-Time Control Tasks. International Journal of Innovative Computing, Information and Control (IJICIC), 4(11), pp. 2965-2975.
Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimizing the overhead of feedback scheduling, this paper proposes an efficient feedback scheduling scheme based on feedforward neural networks. Using the optimal solutions obtained offline by mathematical optimization methods, a back-propagation (BP) neural network is designed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. Numerical simulation results show that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling.
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|Item Type:||Journal Article|
|Keywords:||Feedback scheduling, Neural networks, Real, time scheduling, Computational overhead, Embedded control systems|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2008 ICIC International|
|Copyright Statement:||Reproduced in accordance with the copyright policy of the publisher.|
|Deposited On:||16 Jun 2008 00:00|
|Last Modified:||29 Feb 2012 13:47|
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