Inner-Approximating Reachable Sets for Polynomial Systems with Time-Varying Uncertainties

TL;DR

This paper introduces a convex programming method to compute inner-approximations of backward reachable sets for polynomial systems with time-varying uncertainties, enabling safer control design.

Contribution

It presents a novel semi-definite programming approach to synthesize polynomial viscosity super-solutions for inner-approximating backward reachable sets, with proven convergence.

Findings

Method produces convergent inner-approximations in measure.

Semi-definite program always admits solutions under certain conditions.

Examples demonstrate effectiveness and advantages of the approach.

Abstract

In this paper we propose a convex programming based method to address a long-standing problem of inner-approximating backward reachable sets of state-constrained polynomial systems subject to time-varying uncertainties. The backward reachable set is a set of states, from which all trajectories starting will surely enter a target region at the end of a given time horizon without violating a set of state constraints in spite of the actions of uncertainties. It is equal to the zero sub-level set of the unique Lipschitz viscosity solution to a Hamilton-Jacobi partial differential equation (HJE). We show that inner-approximations of the backward reachable set can be formed by zero sub-level sets of its viscosity super-solutions. Consequently, we reduce the inner-approximation problem to a problem of synthesizing polynomial viscosity super-solutions to this HJE. Such a polynomial solution in our method is synthesized by solving a single semi-definite program. We also prove that polynomial solutions to the formulated semi-definite program exist and can produce a convergent sequence of inner-approximations to the interior of the backward reachable set in measure under appropriate assumptions. This is the main contribution of this work. Several illustrative examples demonstrate the merits of our approach.