Safety-critical Policy Iteration Algorithm for Control under Model Uncertainty

Safety-critical Policy Iteration Algorithm for Control under Model Uncertainty

DOI: 

https://doi.org/10.30564/aia.v4i1.4361

Abstract

Safety is an important aim in designing safe-critical systems. To design such systems, many policy iterative algorithms are introduced to find safe optimal controllers. Due to the fact that in most practical systems, finding accurate information from the system is rather impossible, a new online training method is presented in this paper to perform an iterative reinforcement learning based algorithm using real data instead of identifying system dynamics. Also, in this paper the impact of model uncertainty is examined on control Lyapunov functions (CLF) and control barrier functions (CBF) dynamic limitations. The Sum of Square program is used to iteratively find an optimal safe control solution. The simulation results which are applied on a quarter car model show the efficiency of the proposed method in the fields of optimality and robustness.

Keywords: 

Safe-critical, Optimal controller, Reinforcement learning, Lyapunov, Sum-of-Square
𝑢𝑻𝑯𝑬𝑹 𝑳𝑰𝑡𝑲𝑺:
DocDroid
https://docdro.id/WhKQjoj
Slideshare
https://www.slideshare.net/ssusere89c6f/artificial-intelligence-advances-vol4-iss1-april-2022
Scribd
https://www.scribd.com/document/624708961/Artificial-Intelligence-Advances-Vol-4-Iss-1-April-2022
YouTube
https://youtu.be/07cwsJVRA3g

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