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

This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlin-ear discrete-time systems.The nonlinear system is modeled using linear parameter-varying(LPV)systems.A model-based proba-bilistic 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 repre-sentation is provided in terms of state and scheduling data as well as a decision variable.It is shown that the variance of the closed-loop system,as well as the probability of safety satisfaction,depends on the decision variable and the noise covariance.A min-imum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision vari-able 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 mini-mizes the variance of the state of the closed-loop system with respect to the safe set,and thus minimizes the risk of safety viola-tion.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 rich-ness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety viola-tion.Two simulation examples along with an experimental vali-dation on an autonomous vehicle are provided to show the effec-tiveness of the presented approach.