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

作　　者：Adolfo Perrusquía Weisi Guo 

机构地区：[1]IEEE [2]the School of Aerospace,Transport and Manufacturing,Cranfield University,Bedford,UK

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第1期90-102,共13页自动化学报（英文版）

基　　金：supported by the Royal Academy of Engineering and the Office of the Chie Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme。

摘　　要：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 realworld 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.

关 键 词：Experience inference hippocampus learning system linear time-variant(LTV)systems neocortex/striatum learning systems nonlinear systems optimal control 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]