In this paper, we study the effects of radiation-induced soft errors in iterative learning control(ILC) and present the compensation techniques to make the ILC systems robust against soft errors. Soft errors are transient faults, which occur temporarily in memories where the energetic particles strike the sensitive region in the transistors mainly under abnormal conditions such as high radiation, high temperature, and high pressure. These soft errors can cause bit value changes without any notification to the controller, affect the stability of the system, and result in catastrophic consequences. First, we investigate and analyze the effects of soft errors in the ILC systems. Our analytical study shows that when a single soft error occurs in the output data from the ILC, the performance of the learning control is significantly degraded. Second, we propose novel learning methods by incorporating the existing techniques across the system abstraction levels in the ILC to compensate for soft-error-induced incorrect output. The occurrence of soft errors is estimated by using a monotonic convergence of the erroneous outputs in a cross-layer manner, and our proposed methods can significantly reduce these negative impacts on the system performance. Under the assumption of soft error occurrence, our analytic study has proved the convergence of the proposed methods in the ILC systems and our simulation results show the effectiveness of the proposed methods to efficiently reduce the impacts of soft-error-induced outputs in the ILC systems.
|Number of pages||16|
|Journal||International Journal of Computers, Communications and Control|
|Publication status||Published - 2019|
Bibliographical noteFunding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A09083894), and also supported by the National Research Foundation of Korea (NRF) funded by the Korean Government (MSIP) (NRF-2016R1A5A1012966).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
- Computational Theory and Mathematics