Memoryless concretization relation
Abstract: We introduce the concept of memoryless concretization relation (MCR) to describe abstraction within the context of controller synthesis. This relation is a specific instance of alternating simulation relation (ASR), where it is possible to simplify the controller architecture. In the case of ASR, the concretized controller needs to simulate the concurrent evolution of two systems, the original and abstract systems, while for MCR, the designed controllers only need knowledge of the current concrete state. We demonstrate that the distinction between ASR and MCR becomes significant only when a non-deterministic quantizer is involved, such as in cases where the state space discretization consists of overlapping cells. We also show that any abstraction of a system that alternatingly simulates a system can be completed to satisfy MCR at the expense of increasing the non-determinism in the abstraction. We clarify the difference between the MCR and the feedback refinement relation (FRR), showing in particular that the former allows for non-constant controllers within cells. This provides greater flexibility in constructing a practical abstraction, for instance, by reducing non-determinism in the abstraction. Finally, we prove that this relation is not only sufficient, but also necessary, for ensuring the above properties.
- G. Reissig, A. Weber, and M. Rungger, “Feedback refinement relations for the synthesis of symbolic controllers,” IEEE Transactions on Automatic Control, vol. 62, no. 4, pp. 1781–1796, 2016.
- O. Kupferman and M. Y. Vardi, “Model checking of safety properties,” Formal methods in system design, vol. 19, pp. 291–314, 2001.
- R. Alur, T. A. Henzinger, O. Kupferman, and M. Y. Vardi, “Alternating refinement relations,” in CONCUR’98 Concurrency Theory: 9th International Conference Nice, France, September 8–11, 1998 Proceedings 9. Springer, 1998, pp. 163–178.
- M. Rungger and M. Zamani, “Scots: A tool for the synthesis of symbolic controllers,” in Proceedings of the 19th international conference on hybrid systems: Computation and control, 2016, pp. 99–104.
- A. Borri, G. Pola, and M. D. Di Benedetto, “Design of symbolic controllers for networked control systems,” IEEE Transactions on Automatic Control, vol. 64, no. 3, pp. 1034–1046, 2018.
- L. N. Egidio, T. A. Lima, and R. M. Jungers, “State-feedback abstractions for optimal control of piecewise-affine systems,” in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022, pp. 7455–7460.
- R. Majumdar, N. Ozay, and A.-K. Schmuck, “On abstraction-based controller design with output feedback,” in Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control, 2020, pp. 1–11.
- A. Girard, “Controller synthesis for safety and reachability via approximate bisimulation,” Automatica, vol. 48, no. 5, pp. 947–953, 2012.
- E. Dallal, A. Colombo, D. Del Vecchio, and S. Lafortune, “Supervisory control for collision avoidance in vehicular networks with imperfect measurements,” in 52nd IEEE Conference on Decision and Control. IEEE, 2013, pp. 6298–6303.
- L. Grune and O. Junge, “Approximately optimal nonlinear stabilization with preservation of the lyapunov function property,” in 2007 46th IEEE Conference on Decision and Control. IEEE, 2007, pp. 702–707.
- K. Hsu, R. Majumdar, K. Mallik, and A.-K. Schmuck, “Lazy abstraction-based control for safety specifications,” in 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018, pp. 4902–4907.
- B. Yordanov, J. Tumova, I. Cerna, J. Barnat, and C. Belta, “Temporal logic control of discrete-time piecewise affine systems,” IEEE Transactions on Automatic Control, vol. 57, no. 6, pp. 1491–1504, 2011.
- A. Girard, “Low-complexity quantized switching controllers using approximate bisimulation,” Nonlinear Analysis: Hybrid Systems, vol. 10, pp. 34–44, 2013.
- K. Hsu, R. Majumdar, K. Mallik, and A.-K. Schmuck, “Lazy abstraction-based controller synthesis,” in International Symposium on Automated Technology for Verification and Analysis. Springer, 2019, pp. 23–47.
- B. Legat, R. M. Jungers, and J. Bouchat, “Abstraction-based branch and bound approach to q-learning for hybrid optimal control,” in Learning for Dynamics and Control. PMLR, 2021, pp. 263–274.
- J. Calbert, B. Legat, L. N. Egidio, and R. Jungers, “Alternating simulation on hierarchical abstractions,” in IEEE Conference on Decision and Control (CDC), 2021, pp. 593–598.
- G. Gallo, G. Longo, S. Pallottino, and S. Nguyen, “Directed hypergraphs and applications,” Discrete applied mathematics, vol. 42, no. 2-3, pp. 177–201, 1993.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.