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Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

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A. Perrusquía and W. Guo, “Optimal control of nonlinear systems using experience inference human-behavior learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 90–102, Jan. 2023. doi: 10.1109/JAS.2023.123009
Citation: A. Perrusquía and W. Guo, “Optimal control of nonlinear systems using experience inference human-behavior learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 90–102, Jan. 2023. doi: 10.1109/JAS.2023.123009

Optimal Control of Nonlinear Systems Using Experience Inference Human-Behavior Learning

doi: 10.1109/JAS.2023.123009
Funds:  This work was supported by the Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme
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  • Safety critical control is often trained in a simulated environment to mitigate risk. Subsequent migration of the biased controller requires further adjustments. In this paper, an experience inference human-behavior learning is proposed to solve the migration problem of optimal controllers applied to real-world nonlinear systems. The approach is inspired in the complementary properties that exhibits the hippocampus, the neocortex, and the striatum learning systems located in the brain. The hippocampus defines a physics informed reference model of the real-world nonlinear system for experience inference and the neocortex is the adaptive dynamic programming (ADP) or reinforcement learning (RL) algorithm that ensures optimal performance of the reference model. This optimal performance is inferred to the real-world nonlinear system by means of an adaptive neocortex/striatum control policy that forces the nonlinear system to behave as the reference model. Stability and convergence of the proposed approach is analyzed using Lyapunov stability theory. Simulation studies are carried out to verify the approach.


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    • The paper gives a solution to the migration problem from simulations to real-world experiments to ensure safety critical control
    • A novel optimal control solution of nonlinear systems based on a human-behaviour learning approach
    • The algorithm does not require to solve a HJB equation and does not require knowledge of the real parameters of the system
    • The final control policy applied to the real system is unbiased to simulation constraints


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