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Optimal Control of Nonlinear Systems Using Experience Inference Human-Behavior Learning
Adolfo Perrusquía; Weisi Guo
Source PublicationIEEE/CAA Journal of Automatica Sinica
AbstractSafety 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.
KeywordExperience inference hippocampus learning system linear time-variant (LTV) systems neocortex/striatum learning systems nonlinear systems optimal control
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Document Type期刊论文
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
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GB/T 7714
Adolfo Perrusquía,Weisi Guo. Optimal Control of Nonlinear Systems Using Experience Inference Human-Behavior Learning[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(1):90-102.
APA Adolfo Perrusquía,&Weisi Guo.(2023).Optimal Control of Nonlinear Systems Using Experience Inference Human-Behavior Learning.IEEE/CAA Journal of Automatica Sinica,10(1),90-102.
MLA Adolfo Perrusquía,et al."Optimal Control of Nonlinear Systems Using Experience Inference Human-Behavior Learning".IEEE/CAA Journal of Automatica Sinica 10.1(2023):90-102.
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