Abstract
The emerging scenarios of cyber-physical systems (CPS), such as autonomous vehicles, require implementing complex functionality with limited resources, as well as high performances. This paper considers a common setup in which multiple control and non-control tasks share one processor, and proposes a dual-mode strategy. The control task switches between two sampling periods when rejecting (coping with) a disturbance. We create an optimisation framework looking for the switching sampling periods and time instants that maximise the control performance (indexed by settling time) and resource efficiency (indexed by the number of tasks that are schedulable on the processor). The latter objective is enabled with schedulability analysis tailored for the dual-mode model. Experimental results show that (i) given a set of tasks, the proposed strategy improves the control performances whilst retaining schedulability; and (ii) given requirements on the control performances, the proposed strategy is able to schedule more tasks.
- Karl-Erik Årzén. 1999. A simple event-based PID controller. In Proc. 14th IFAC World Congress, Vol. 18. 423--428Google Scholar
- Karl-Erik Årzén, Bo Bernhardsson, Johan Eker, Anton Cervin, Klas Nilsson, Patrik Persson, and Lui Sha. 1999. Integrated control and scheduling. Department of Automatic Control, Lund Institute of Technology, Sweden, Tech. Rep. ISRN LUTFD2/TFRT--7586--SE (1999).Google Scholar
- Karl-Erik Årzén, Anton Cervin, Johan Eker, and Lui Sha. 2000. An introduction to control and scheduling co-design. In Proceedings of the 39th IEEE Conference on Decision and Control, 2000., Vol. 5. IEEE, 4865--4870.Google Scholar
Cross Ref
- Giuseppe Ascia, Vincenzo Catania, and Maurizio Palesi. 2006. A multi-objective genetic approach to mapping problem on network-on-chip. J. UCS 12, 4 (2006), 370--394.Google Scholar
- Karl J Åström and Björn Wittenmark. 2013. Computer-controlled Systems: Theory and Design. Courier Corporation.Google Scholar
- Sanjoy Baruah. 2016. Schedulability analysis of mixed-criticality systems with multiple frequency specifications. In 2016 International Conference on Embedded Software (EMSOFT). IEEE, 1--10.Google Scholar
Digital Library
- S. K. Baruah and A. Burns. 2006. Sustainable schedulability analysis. In Proc. of IEEE Real-Time Systems Symposium (RTSS). 159--168.Google Scholar
- Enrico Bini and Giuseppe Buttazzo. 2014. The optimal sampling pattern for linear control systems. IEEE Trans. Automat. Control 59, 1 (2014), 78--90.Google Scholar
Cross Ref
- Enrico Bini and Giorgio C. Buttazzo. 2005. Measuring the performance of schedulability tests. Real-Time Systems 30, 1--2 (2005), 129--154.Google Scholar
Digital Library
- A. Burns and A. J. Wellings. 2016. Analysable Real-Time Systems Programmed in Ada. ISBN: 9781530265503.Google Scholar
- R. Castane, P. Marti, M. Velasco, A. Cervin, and D. Henriksson. 2006. Resource management for control tasks based on the transient dynamics of closed-loop systems. In 18th Euromicro Conference on Real-Time Systems (ECRTS).Google Scholar
- Anton Cervin, Johan Eker, Bo Bernhardsson, and Karl-Erik Årzén. 2002. Feedback-feedforward scheduling of control tasks. Real-Time Systems 23, 1--2 (2002), 25--53.Google Scholar
Digital Library
- Anton Cervin, Manel Velasco, Pau Martí, and Antonio Camacho. 2011. Optimal online sampling period assignment: Theory and experiments. IEEE Transactions on Control Systems Technology 19, 4 (2011), 902--910.Google Scholar
Cross Ref
- Wanli Chang and Samarjit Chakraborty. 2016. Resource-aware automotive control systems design: A cyber-physical systems approach. Foundations and Trends in Electronic Design Automation 10, 4 (2016), 249--369.Google Scholar
Digital Library
- Richard C. Dorf and Robert H. Bishop. 2011. Modern Control Systems. Pearson.Google Scholar
- Paul Emberson and Iain Bate. 2008. Extending a task allocation algorithm for graceful degradation of real-time distributed embedded systems. In 2008 Real-Time Systems Symposium. IEEE, 270--279.Google Scholar
Digital Library
- Paul Emberson and Iain Bate. 2010. Stressing search with scenarios for flexible solutions to real-time task allocation problems. IEEE Transactions on Software Engineering 36, 5 (2010), 704--718.Google Scholar
Digital Library
- Luca Greco, Daniele Fontanelli, and Antonio Bicchi. 2011. Design and stability analysis for anytime control via stochastic scheduling. IEEE Trans. Automat. Control 56, 3 (2011), 571--585.Google Scholar
Cross Ref
- Moncef Hamdaoui and Parameswaran Ramanathan. 1995. A dynamic priority assignment technique for streams with (m, k)-firm deadlines. IEEE Transactions on Computers 44, 12 (1995), 1443--1451.Google Scholar
Digital Library
- Hai Lin and Panos Antsaklis. 2009. Stability and stabilizability of switched linear systems: A survey of recent results. IEEE Trans. Automat. Control 54, 2 (2009), 308--322.Google Scholar
Cross Ref
- Paris Mesidis and Leandro Soares Indrusiak. 2011. Genetic mapping of hard real-time applications onto NoC-based MPSoCs - a first approach. In 6th International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC). IEEE, 1--6.Google Scholar
Cross Ref
- Charles L. Phillips and H. Troy Nagle. 2007. Digital Control System Analysis and Design. Prentice Hall Press.Google Scholar
- Adrian Racu and Leandro Soares Indrusiak. 2012. Using genetic algorithms to map hard real-time on NoC-based systems. In 7th International Workshop on Reconfigurable and Communication-Centric Systems-on-Chip (ReCoSoC). IEEE, 1--8.Google Scholar
Cross Ref
- P. Ramanathan. 1997. Graceful degradation in real-time control applications using (m, k)-firm guarantee. Proceedings of IEEE 27th International Symposium on Fault Tolerant Computing (1997), 132--141.Google Scholar
Digital Library
- Danbing Seto, John P. Lehoczky, Lui Sha, and Kang G. Shin. 1996. On task schedulability in real-time control systems. In 17th IEEE Real-Time Systems Symposium, 1996. IEEE, 13--21.Google Scholar
Cross Ref
- Takao Yokota, Mitsuo Gen, and Yin-Xiu Li. 1996. Genetic algorithm for non-linear mixed integer programming problems and its applications. Computers 8 Industrial Engineering 30, 4 (1996), 905--917.Google Scholar
- Shuai Zhao. 2018. A FIFO Spin-based Resource Control Framework for Symmetric Multiprocessing. Ph.D. Dissertation. University of York.Google Scholar
Index Terms
A Dual-Mode Strategy for Performance-Maximisation and Resource-Efficient CPS Design
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