Fuzzy Control
Feedback–Feedforward Scheduling of Control Tasks
Real-Time Systems
Analysis of a Reservation-Based Feedback Scheduler
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Managing Quality-of-Control Performance Under Overload Conditions
ECRTS '04 Proceedings of the 16th Euromicro Conference on Real-Time Systems
Efficient Reclaiming in Reservation-Based Real-Time Systems with Variable Execution Times
IEEE Transactions on Computers
Integrated computation, communication and control: towards next revolution in information technology
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
Neural network based feedback scheduling of multitasking control systems
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Anytime iterative optimal control using fuzzy feedback scheduler
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Neural network based feedback scheduler for networked control system with flexible workload
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Fuzzy logic based feedback scheduler for embedded control systems
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
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Today's embedded systems representatively feature computing resource constraints as well as workload uncertainty. This gives rise to the increasing demand of integrating control and scheduling in control applications that are built upon embedded systems. To address the impact of uncertain computing resource availability on quality of control (QoC), an intelligent control theoretic approach to feedback scheduling is proposed based on fuzzy logic control technology. The case with one single control task that competes for CPU resources with other non-control tasks is considered. The sampling period of the control task is dynamically adjusted. The goal is to provide runtime adaptation and flexible QoC management in the presence of CPU resource constraint and workload uncertainty. Preliminary simulation results argue that the fuzzy feedback scheduler is effective in managing QoC in real-time embedded control applications.