Non-Conservative Data-driven Safe Control Design for Nonlinear Systems with Polyhedral Safe Sets

Abstract

This paper presents a data-driven nonlinear safe control design approach for discrete-time systems under parametric uncertainties and additive disturbances. We first characterize a new control structure from which a data-based representation of closed-loop systems is obtained. This data-based closed-loop system is composed of two parts: 1) a parametrized linear closed-loop part and a parametrized nonlinear remainder closed-loop part. We show that using the standard practice or learning a robust controller to ensure safety while treating the remaining nonlinearities as disturbances brings about significant challenges in terms of computational complexity and conservatism. To overcome these challenges, we develop a novel nonlinear safe control design approach in which the closed-loop nonlinear remainders are learned, rather than canceled, in a control-oriented fashion while preserving the computational efficiency. To this end, a primal-dual optimization framework is leveraged in which the control gains are learned to enforce the second-order optimality on the closed-loop nonlinear remainders. This allows us to account for nonlinearities in the design for the sake of safety rather than treating them as disturbances. This new controller parameterization and design approach reduces the computational complexity and the conservatism of designing a safe nonlinear controller. A simulation example is then provided to show the effectiveness of the proposed data-driven controller.