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

作     者：Babak Esmaeili Hamidreza Modares Babak Esmaeili;Hamidreza Modares

作者机构：Department of Mechanical EngineeringMichigan State UniversityEast LansingMI 48863 USA 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2024年第11卷第9期

页      面：1918-1932页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

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

主　　题：Data-driven control linear parameter-varying systems probabilistic control safe control 

摘      要：This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time *** 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 *** 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 *** 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 *** 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 *** the certainty-equivalent approach that results in a risk-neutral control design,the minimum-variance method leads to a risk-averse control *** 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 *** simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.