Abstract
Cyber-physical systems (CPS) are ones in which computation, communication, and control each have significant impact on the performance of the system. In this chapter, we discuss systems where coupling between the dynamics of the system, and how it is controlled, can be considered alongside the computational efforts required to design and control these systems. The topics include various models that can be used to describe the system’s performance, examples of architectures that have been employed to permit system design, and some analyses that have proved useful in those architectures. This chapter sets the stage for continued research in this area as more kinds of models and architectures are considered.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
There are myriad examples where multiple state inputs occur between control input opportunities, so this statement is not globally true, but is valid in this case.
References
K. Zhang, J. Sprinkle, R.G. Sanfelice, A hybrid model predictive controller for path planning and path following, in Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, 2015, pp. 139–148
E. Narby, Modeling and estimation of dynamic tire properties. Master’s thesis, Linköpings Universitet, Linköpings, 2006
G. Walsh, D. Tilbury, S. Sastry, R. Murray, J.-P. Laumond, Stabilization of trajectories for systems with nonholonomic constraints. Autom. Control IEEE Trans. 39(1), 216–222 (1994)
J. Sprinkle, The carsimple repository of simple car models, 2021 [Online]. Available: https://github.com/sprinkjm/carsimple.git
S. Whitsitt, J. Sprinkle, A passenger comfort controller for an autonomous ground vehicle, in 51st IEEE Conference on Decision and Control, 2012, pp. 3380–3385 [Online]. Available: https://doi.org/10.1109/CDC.2012.6426049
K. Zhang, J. Sprinkle, R.G. Sanfelice, Computationally-aware control of autonomous vehicles: a hybrid model predictive control approach. Auton. Robot. 503–517 (2015) [Online]. Available: https://doi.org/10.1007/s10514-015-9469-5
K. Zhang, J. Sprinkle, R.G. Sanfelice, Computationally-aware switching criteria for hybrid model predictive control of cyber-physical systems. IEEE Trans. Autom. Sci. Eng. 13, 479–490 (2016) [Online]. Available: https://doi.org/10.1109/TASE.2016.2523341
Y. Kim, D. Broman, J. Cai, A. Shrivastaval, Wcet-aware dynamic code management on scratchpads for software-managed multicores, in 2014 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS), 2014, pp. 179–188
Acknowledgements
The results summarized in this chapter are due thanks to the exploration of Kun Zhang, in collaboration with Ricardo Sanfelice. Additional comments from and discussion with Nathalie Risso formed much of the basis upon which this tutorial has been presented visually when documenting challenges and opportunities in computationally aware control of CPS. This work is supported by the National Science Foundation, awards CNS-1544395 and CNS-2111688.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sprinkle, J. (2023). Models, Architectures, and Analysis for Computationally Aware CPS. In: Prandini, M., Sanfelice, R.G. (eds) Computation-Aware Algorithmic Design for Cyber-Physical Systems. Systems & Control: Foundations & Applications. Birkhäuser, Cham. https://doi.org/10.1007/978-3-031-43448-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-43448-8_2
Published:
Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-031-43447-1
Online ISBN: 978-3-031-43448-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)