Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems  

作　　者：Babak Esmaeili Hamidreza Modares 

机构地区：[1]Department of Mechanical Engineering,Michigan State University,East Lansing,MI 48863 USA

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第9期1918-1932,共15页自动化学报（英文版）

基　　金：supported in part by the Department of Navy award (N00014-22-1-2159);the National Science Foundation under award (ECCS-2227311)。

摘　　要：This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems.The nonlinear system is modeled using linear parameter-varying(LPV)systems.A model-based probabilistic safe controller is first designed to guarantee probabilisticλ-contractivity(i.e.,stability and invariance)of the LPV system with respect to a given polyhedral safe set.To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model,its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable.It is shown that the variance of the closedloop system,as well as the probability of safety satisfaction,depends on the decision variable and the noise covariance.A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level.This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set,and thus minimizes the risk of safety violation.Unlike the certainty-equivalent approach that results in a risk-neutral control design,the minimum-variance method leads to a risk-averse control design.It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation.Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.

关 键 词：Data-driven control linear parameter-varying systems probabilistic control safe control 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]