Abstract
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.
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References
Chow, M.-Y., Tipsuwan, Y.: Gain Adaptation of Networked DC Motor Controllers Based on QOS Variations. IEEE Trans. on Industrial Electronics 50(5), 936–943 (2003)
Xia, F., Dai, X., Wang, Z., Sun, Y.: Feedback Based Network Scheduling of Networked Control Systems. In: Proc. 5th ICCA, Budapest, Hungary (2005)
Xia, F., Wang, Z., Sun, Y.: Integrated Computation, Communication and Control: Towards Next Revolution in Information Technology. In: Das, G., Gulati, V.P. (eds.) CIT 2004. LNCS, vol. 3356, pp. 117–125. Springer, Heidelberg (2004)
Tipsuwan, Y., Chow, M.-Y.: Control methodologies in networked control systems. Control Eng. Practice 11(10), 1099–1111 (2003)
Lu, C., Stankovic, J., Tao, G., Son, S.H.: Feedback control real-time scheduling: framework, modeling, and algorithms. Real-time Systems 23(1/2), 85–126 (2002)
Cervin, A., Eker, J., Bernhardsson, B., Årzén, K.-E.: Feedback-Feedforward Scheduling of Control Tasks. Real-Time Systems 23(1), 25–53 (2002)
Abeni, L., Palopoli, L., Lipari, G., Walpole, J.: Analysis of a Reservation-Based Feedback Scheduler. In: Proc. 23rd IEEE RTSS, Austin, Texas, pp. 71–80 (2002)
Xia, F., Sun, Y.: Neural Network Based Feedback Scheduling of Multitasking Control Systems. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3682, pp. 193–199. Springer, Heidelberg (2005)
Sun, Z., Zhang, Z., Deng, Z.: Intelligent Control Theory and Technology. Tsinghua University Press, Beijing (1997) (in Chinese)
Ryu, S., Rump, C., Qiao, C.: Advances in Internet Congestion Control. IEEE Communications Surveys and Tutorials 5(1), 28–39 (2003)
Liu, Y.-C., Douligeris, C.: Rate Regulation with Feedback Controller in ATM Networks - A Neural Network Approach. IEEE Journal on Selected Area in Communications 15(2), 200–208 (1997)
Bhattacharya, A., Parlos, A., Atiya, A.: Prediction of MPEG-Coded Video Source Traffic Using Recurrent Neural Networks. IEEE Trans. on Signal Processing 51(8), 2177–2190 (2003)
Liu, D., Du, J., Zhao, Y., Song, N.: Study on the Time-Delay of Internet-based Industry Process Control System. In: Proc. 5th WCICA, Hangzhou, China, vol. 2, pp. 1376–1380 (2004)
Xiao, L., Johansson, M., Hindi, H., Boyd, S., Goldsmith, A.: Joint Optimization of Communication Rates and Linear Systems. IEEE Trans. on Automatic Control 48(1), 148–153 (2003)
Li, S., Cao, Y.-Y., Wang, Y., Sun, Y.: Robust H ∞ Control of Uncertain Markovian Jump Systems with Mode-Dependent Time-Delays. In: Proc. 16th IFAC World Congress, Prague, Czech Republic (2005)
Abdelzaher, T., Atkins, E., Shin, K.: QoS Negotiation in Real-Time Systems and Its Application to Automated Flight Control. IEEE Trans. on Computers 49(11), 1170–1183 (2000)
Beccari, G., Caselli, S., Reggiani, M., Zanichelli, F.: Rate modulation of soft real-time tasks in autonomous robot control systems. In: Proc. 11th ECRTS, York, England, pp. 21–28 (1999)
Eker, J., Hagander, P., Årzén, K.-E.: A feedback scheduler for real-time controller tasks. Control Engineering Practice 8(12), 1369–1378 (2000)
Xia, F., Liu, L., Sun, Y.: Flexible Quality-of-Control Management in Embedded Systems Using Fuzzy Feedback Scheduling. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 624–633. Springer, Heidelberg (2005)
Xia, F., Sun, Y.: Anytime Iterative Optimal Control Using Fuzzy Feedback Scheduler. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3682, pp. 350–356. Springer, Heidelberg (2005)
Henriksson, D., Cervin, A., Åkesson, J., Årzén, K.-E.: Feedback Scheduling of Model Predictive Controllers. In: Proc. 8th IEEE RTAS, San Jose, CA, pp. 207–216 (2002)
Branicky, M., Philips, S., Zhang, W.: Scheduling and feedback co-design for networked control systems. In: Proc. 41st IEEE CDC, Las Vegas, pp. 1211–1217 (2002)
Park, H., Kim, Y., Kim, D.-S., Kwon, W.H.: A Scheduling Method for Network-based Control Systems. IEEE Trans. on Control System Tech. 10(3), 318–330 (2002)
Velasco, M., Fuertes, J., Lin, C., Marti, P., Brandt, S.: A Control Approach to Bandwidth Management in Networked Control Systems. In: Proc. 30th IEEE IECON, Busan, Korea (2004)
Xia, F., Yin, H., Wang, Z., Sun, Y.: Theory and Practice of Real-time Scheduling in Networked Control Systems. In: Proc. 17th Chinese Control and Decision Conference, Harbin, China (2005) (in Chinese)
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Xia, F., Li, S., Sun, Y. (2005). Neural Network Based Feedback Scheduler for Networked Control System with Flexible Workload. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_36
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DOI: https://doi.org/10.1007/11539117_36
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