Neural Network Based Feedback Scheduler for Networked Control System with Flexible Workload

• Published in: Advances in Natural Computation pp. 242 - 251
• Main Authors: Xia, Feng, Li, Shanbin, Sun, Youxian
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Congestion Control; Control Loop; Control Performance; Control Period; Exact sciences and technology; Network Resource; Availability; Time varying system; Modular architecture; Probabilistic approach; Scheduling; Neural network; Fast algorithm; Non determinism; Non deterministic system; Remote teaching; Actuator; Workload
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_36

Most control applications closed over a shared network are suffering from the time-varying characteristics of flexible network workload. This gives rise to non-deterministic availability of communication resources and may significantly impact the control performance. In the context of integrating control and scheduling, a novel feedback scheduler based on neural networks is suggested. With a modular architecture, the proposed feedback scheduler mainly consists of a monitor, a predictor, a regulator and an actuator. An online learning Elman neural network is employed to predict the network conditions, and then the control period is dynamically adjusted in response to estimated available network utilization. A fast algorithm for period regulation is employed. Preliminary simulation results show that the proposed feedback scheduler is effective in managing workload variations and can provide runtime flexibility to networked control applications.