Feedback–Feedforward Scheduling of Control Tasks
Real-Time Systems
Feedback Scheduling of Model Predictive Controllers
RTAS '02 Proceedings of the Eighth IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'02)
Linear and Nonlinear Iterative Learning Control (Lecture Notes in Control and Information Sciences, 291)
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
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
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The problem of neural network based iterative learning control (NNILC) in a resource-constrained environment with workload uncertainty is examined from the real-time implementation perspective. Thanks to the iterative nature of the NNILC algorithm, it is possible to abort the optimization routine before it reaches the optimum. Taking into account the impact of resource constraints, a feedback scheduling approach is suggested, with the primary goal of maximize the control performance. The execution time of the NNILC task is dynamically adjusted to achieve a desired CPU utilization level. Thus a tradeoff is done between the available CPU time and the control performance. For the sake of easy implementation, a practical solution with a dynamic iteration stop criterion is proposed. Preliminary simulation results argue that the proposed approach is efficient and delivers better performance in the face of workload variations than the traditional NNILC algorithm.