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
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
NN-Based iterative learning control under resource constraints: a feedback scheduling approach
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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The paper presents some preliminary results on dynamic scheduling of model predictive controllers (MPCs). In an MPC, the control signal is obtained by on-line optimization of a cost function, and the MPC task may experience very large variations in execution time from sample to sample. Unique to this application, the cost function offers an explicit, on-line quality-of-service measure for the task. Based on this insight, a feedback scheduling strategy for multiple MPCs is proposed, where the scheduler allocates CPU time to the tasks according to the current values of the cost functions. Since the MPC algorithm is iterative, the feedback scheduler may also abort a task prematurely to avoid excessive input-output latency. A case study is presented, where the new approach is compared to conventional fixed-priority and earliest-deadline-first scheduling. General problems related to the real-time implementation of MPCs are also discussed.