Computer-controlled systems (3rd ed.)
Computer-controlled systems (3rd ed.)
Neural network design
Feedback–Feedforward Scheduling of Control Tasks
Real-Time Systems
Integrated computation, communication and control: towards next revolution in information technology
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
Experiments with simple neural networks for real-time control
IEEE Journal on Selected Areas in Communications
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
Flexible quality-of-control management in embedded systems using fuzzy feedback scheduling
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - 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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To cope with resource constraints in multitasking control systems, feedback scheduling is emerging as an important technique for integrating control and scheduling. The ability of neural networks (NNs) as good and robust nonlinear function approximators, reducing the computation time as compared against algorithmic solutions, suggests applying them to the feedback scheduling problem. A novel, simple and intelligent feedback scheduler is presented using a feedforward NN. The algorithmic optimizer is utilized as a teacher to generate data samples for NN training. The role of the NN based feedback scheduler is to provide a good approximation to the optimal solution and online adjust the sampling period of each control task so that the overall system performance is maximized in the face of workload variations. The performance of the NN approach is evaluated through co-simulations of the scheduler, controllers and process dynamics.