A Hamilton-Jacobi-Bellman Approach to Ellipsoidal Approximations of Reachable Sets for Linear Time-Varying Systems

• Published in: IEEE transactions on automatic control Vol. 70; no. 6; pp. 3633 - 3648
• Main Authors: Liu, Vincent, Manzie, Chris, Dower, Peter
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation; Bellman theory; Control systems design; Dynamical systems; Ellipsoids; Hamilton–Jacobi (HJ) equations; Linear systems; Nonlinear systems; Optimal control; Polynomials; Reachability analysis; Symmetric matrices; System dynamics; Time varying control systems; Time-varying systems; Trajectory; Viscosity
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2024.3512619

Reachable sets for a dynamical system describe collections of system states that can be reached in finite time, subject to system dynamics. They can be used to guarantee goal satisfaction in controller design or to verify that unsafe regions will be avoided. However, conventional grid-based methods for computing these sets suffer from the curse of dimensionality, which typically prohibits their use for systems with more than a small number of states, even if they are linear. In this article, we demonstrate that local viscosity supersolutions and subsolutions of a Hamilton-Jacobi-Bellman equation can be used to generate, respectively, under-approximating and over-approximating reachable sets for time-varying nonlinear systems. Based on this observation, we derive dynamics for a union and intersection of ellipsoidal sets that, respectively, under-approximate and over-approximate the reachable set for linear time-varying systems subject to an ellipsoidal input constraint and an ellipsoidal terminal (or initial) set. The dynamics for these ellipsoids can be selected to ensure that their boundaries coincide with the boundary of the exact reachable set along a collection of solutions of the system. The ellipsoids can be generated with polynomial computational complexity in the number of states, making our approximation scheme computationally tractable for continuous-time linear time-varying systems of relatively high dimension.