/Resilient_Observers_Comparison

Primary LanguageMATLAB

Resilient_Observers_Comparison

Good Resilient Observes in literature and my resilent observers will be tested here.

If you use the codes, please cite

@INPROCEEDINGS{9482913,  
   author={Zheng, Yu and Anubi, Olugbenga Moses},  
   booktitle={2021 American Control Conference (ACC)},   
   title={Attack-Resilient Weighted $\ell_{1}$ Observer with Prior Pruning},  
   year={2021},  
   volume={},  
   number={},  
   pages={340-345},  
   doi={10.23919/ACC50511.2021.9482913}}

Or:

@incollection{zheng2022resilient,
  title={Resilient Observer Design for Cyber-Physical Systems with Data-Driven Measurement Pruning},
  author={Zheng, Yu and Anubi, Olugbenga Moses},
  booktitle={Security and Resilience in Cyber-Physical Systems: Detection, Estimation and Control},
  pages={85--117},
  year={2022},
  publisher={Springer}
}

Any questions, please contact Yu Zheng (yzheng6@fsu.edu).

Simulation setup:

System_model_discrete_2021.slx

system_setup

All parts (in blue blocks) are modulized, you will not have trouble to use them seperately.

Tested observers

Please refer to "run_model.m" for parameter settings, "System_model_discrete_2021.slx" for the exmaples of the usage of observers.

1. L1 decoder

Fawzi, Hamza, Paulo Tabuada, and Suhas Diggavi. "Secure estimation and control for cyber-physical systems under adversarial attacks." IEEE Transactions on Automatic control 59.6 (2014): 1454-1467.

Parameter preparison: Resilent_Optimizer/opti_params.m
Solver: Resilent_Optimizer/solver_call_unc.m
Usage example: System_model_discrete_2021.slx/Unconstrained l1-based Observer

2. Switched/event triggered Luengbuger observer

  • Lu, An-Yang, and Guang-Hong Yang. "Secure state estimation for cyber-physical systems under sparse sensor attacks via a switched Luenberger observer." Information sciences 417 (2017): 454-464.
  • Shoukry, Yasser, and Paulo Tabuada. "Event-triggered state observers for sparse sensor noise/attacks." IEEE Transactions on Automatic Control 61.8 (2015): 2079-2091.
Parameter preparison: Resilent_Optimizer/ETLO_params.m
Solver: Resilent_Optimizer/solver_event_luenburger.m
Usage example: System_model_discrete_2021.slx/Event-triggered Luenburger Observer

3. Multi-Model Resilient Observer

  • Anubi, Olugbenga Moses, and Charalambos Konstantinou. "Enhanced resilient state estimation using data-driven auxiliary models." IEEE Transactions on Industrial Informatics 16.1 (2019): 639-647.
  • Anubi, Olugbenga Moses, et al. "Multi-Model Resilient Observer under False Data Injection Attacks." 2020 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2020.
Parameter preparison: Resilent_Optimizer/opti_params.m
Solver: Resilent_Optimizer/solver_call.m
Usage example: System_model_discrete_2021.slx/Multi-model Observer

4. Resilient Pruning Observer

  • Yu Zheng, et al. "Attack-Resilient Weighted L1 Observer with Prior Pruning". 2021 American Control Conference. (can be found in papers folder)
  • Zheng, Yu, and Olugbenga Moses Anubi. "Resilient Observer Design for Cyber-Physical Systems with Data-Driven Measurement Pruning." Security and Resilience in Cyber-Physical Systems: Detection, Estimation and Control. Cham: Springer International Publishing, 2022. 85-117.
Parameter preparison: Resilent_Optimizer/opti_params.m
Solver: Resilent_Optimizer/Weighted_L1_solver.m
Pruning algorithm: Resilent_Optimizer/pruning_algorithm.m (reliable_num_attacks.m, pmf_PB.m)
Usage example: System_model_discrete_2021.slx/Weighted l1-based Observer with pruning

Comparison result (estimation errors)

estimation_error Estimation error (LO: luenburger observer, UL1O: unconstrained L1 decoder, MMO: multi-models observer, ETLO: event triggered Luengbuger observer, RPO: Resilient Pruning Observer)

Planned testing observers

5. Robust Local estiamtor + Global fusion

Nakahira, Yorie, and Yilin Mo. "Attack-Resilient H_2, H_\infty, and L1 State Estimator." IEEE Transactions on Automatic Control 63.12 (2018): 4353-4360.

Parameter preparison: Resilent_Optimizer/opti_params.m
Solver: Resilent_Optimizer/local_estimate.m
Usage example: System_model_discrete_2021.slx/H_2 Observer

This one need more tests, current version fails under attack

6. Gramian-based estimator

Chong, Michelle S., Masashi Wakaiki, and Joao P. Hespanha. "Observability of linear systems under adversarial attacks." 2015 American Control Conference (ACC). IEEE, 2015.

7. SMT-based observer

Shoukry, Yasser, et al. "Secure state estimation for cyber-physical systems under sensor attacks: A satisfiability modulo theory approach." IEEE Transactions on Automatic Control 62.10 (2017): 4917-4932.

2021/04/26 update

add real ML localization algorithm, below is the precision of the generated support prior

Prior_precision

The updated comparison result: application_example_result

The performance of designed FDIA: BDD