Robust Control Lyapunov-Value Functions for Nonlinear Disturbed Systems

TL;DR

This paper introduces Robust Control Lyapunov-Value Functions (R-CLVFs) for nonlinear systems with disturbances, providing a systematic way to ensure stability and invariance despite uncertainties, along with computational techniques to handle high-dimensional problems.

Contribution

It extends CLVF construction to disturbed systems, defines the R-CLVF and related concepts, and proposes computational methods like warmstart and decomposition to address complexity.

Findings

R-CLVF identifies the smallest robust control invariant set.

Trade-off between decay rate and stabilizability region demonstrated.

Warmstart and decomposition improve computational efficiency.

Abstract

Control Lyapunov Functions (CLFs) have been extensively used in the control community. A well-known drawback is the absence of a systematic way to construct CLFs for general nonlinear systems, and the problem can become more complex with input or state constraints. Our preliminary work on constructing Control Lyapunov Value Functions (CLVFs) using Hamilton-Jacobi (HJ) reachability analysis provides a method for finding a non-smooth CLF. In this paper, we extend our work on CLVFs to systems with bounded disturbance and define the Robust CLVF (R-CLVF). The R-CLVF naturally inherits all properties of the CLVF; i.e., it first identifies the "smallest robust control invariant set (SRCIS)" and stabilizes the system to it with a user-specified exponential rate. The region from which the exponential rate can be met is called the "region of exponential stabilizability (ROES)." We provide clearer definitions of the SRCIS and more rigorous proofs of several important theorems. Since the computation of the R-CLVF suffers from the "curse of dimensionality," we also provide two techniques (warmstart and system decomposition) that solve it, along with necessary proofs. Three numerical examples are provided, validating our definition of SRCIS, illustrating the trade-off between a faster decay rate and a smaller ROES, and demonstrating the efficiency of computation using warmstart and decomposition.