Managing quality-of-control performance under overload conditions

A common method for dealing with overload conditions in periodic task systems is to reduce the load by enlarging activation periods. When a periodic task implements a digital controller, however, the variation applied on the task period also affects the control law, which needs to be recomputed for the new activation rate. If computing a new control law requires too much time to be performed at runtime, a set of controllers has to be designed offline for different rates and the system has to switch to the proper controller in the presence of an overload condition. In this paper, we present a method for reducing the number of controllers to be designed offline, while still guaranteeing a given control performance. The proposed approach has been integrated with the elastic scheduling theory to promptly react to overload conditions. The effectiveness of the proposed approach has been verified through extensive simulation experiments performed on an inverted pendulum.

[1]  Anton Cervin,et al.  The control server: a computational model for real-time control tasks , 2003, 15th Euromicro Conference on Real-Time Systems, 2003. Proceedings..

[2]  Kang G. Shin,et al.  QoS negotiation in real-time systems and its application to automated flight control , 1997, Proceedings Third IEEE Real-Time Technology and Applications Symposium.

[3]  Richard C. Dorf,et al.  Modern Control Systems, 7th edition , 1995 .

[4]  Karl-Erik Årzén,et al.  Feedback–Feedforward Scheduling of Control Tasks , 2002, Real-Time Systems.

[5]  Feng-Li Lian,et al.  Network design consideration for distributed control systems , 2002, IEEE Trans. Control. Syst. Technol..

[6]  Luís Almeida,et al.  The flexible time-triggered (FTT) paradigm: an approach to QoS management in distributed real-time systems , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[7]  Tei-Wei Kuo,et al.  Load adjustment in adaptive real-time systems , 1991, [1991] Proceedings Twelfth Real-Time Systems Symposium.

[8]  Kang G. Shin,et al.  Adaptation and graceful degradation of control system performance by task reallocation and period adjustment , 1999, Proceedings of 11th Euromicro Conference on Real-Time Systems. Euromicro RTS'99.

[9]  Gerhard Fohler,et al.  Improving quality-of-control using flexible timing constraints: metric and scheduling , 2002, 23rd IEEE Real-Time Systems Symposium, 2002. RTSS 2002..

[10]  Lui Sha,et al.  Elastic feedback control , 2000, Proceedings 12th Euromicro Conference on Real-Time Systems. Euromicro RTS 2000.

[11]  Naresh K. Sinha,et al.  Modern Control Systems , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Francesco Zanichelli,et al.  Rate modulation of soft real-time tasks in autonomous robot control systems , 1999, Proceedings of 11th Euromicro Conference on Real-Time Systems. Euromicro RTS'99.

[13]  Lui Sha,et al.  On task schedulability in real-time control systems , 1996, 17th IEEE Real-Time Systems Symposium.

[14]  Ragunathan Rajkumar,et al.  Experiences with Processor Reservation and Dynamic QOS in Real-Time Mach , 1996 .

[15]  Luigi Palopoli,et al.  Real-time control system analysis: an integrated approach , 2000, Proceedings 21st IEEE Real-Time Systems Symposium.

[16]  Gerhard Fohler,et al.  Jitter compensation for real-time control systems , 2001, Proceedings 22nd IEEE Real-Time Systems Symposium (RTSS 2001) (Cat. No.01PR1420).

[17]  Giuseppe Lipari,et al.  Elastic task model for adaptive rate control , 1998, Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279).

[18]  Giuseppe Lipari,et al.  Elastic Scheduling for Flexible Workload Management , 2002, IEEE Trans. Computers.

[19]  Giorgio C. Buttazzo,et al.  Adaptive Workload Management through Elastic Scheduling , 2002, Real-Time Systems.