Set-based state estimation for discrete-time constrained nonlinear systems: An approach based on constrained zonotopes and DC programming

This paper introduces a novel state estimator designed for discrete-time nonlinear dynamical systems that encompass unknown-but-bounded uncertainties, along with state linear inequality and nonlinear equality constraints. Our algorithm is based on constrained zonotopes (CZs) and employs a DC programming approach (where DC stands for difference of convex functions). Recently, techniques such as mean value extension and first-order Taylor extension have been adapted from zonotopes to facilitate the propagation of CZs across nonlinear mappings. While the resulting algorithms, known as CZMV and CZFO, achieve higher precision compared to the original zonotopic versions, they still exhibit sensitivity to the wrapping and dependency effects inherent in interval arithmetic. These interval-related challenges can be mitigated through the use of DC programming, as it allows for the determination of approximation error bounds by solving optimization problems. One direct advantage of this technique is the elimination of the dependency effect. Our set-membership filter, referred to as CZDC, provides an alternative solution to CZMV and CZFO. To showcase the effectiveness of our proposed approach, we conducted experiments using CZDC on two numerical examples.