Mishra, Vikas Kumar (Technical University of Kaiserslautern, Germany), Athni Hiremath, Sandesh (Technical University of Kaiserslautern), Bajcinca, Naim (University of Kaiserslautern)

Data-Driven Simultaneous Input and State Estimation

Scheduled for presentation during the Regular Session "Linear systems" (WeCA), Wednesday, June 11, 2025, 17:30−17:50,

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

This paper presents a data-driven approach to simultaneously estimating the inputs and states with a delay of a linear discrete-time system. We consider both noise-free and noisy data cases. In the case of noise-free data, we develop an algorithm to reconstruct inputs and states with a delay simultaneously. We note that a system property known as system invertibility plays an important role in developing this algorithm. Furthermore, we prove that the algorithm returns uniquely the inputs and states of the system. Building on this, in the noisy data case, we present a recursive algorithm akin to the celebrated Kalman filter, which estimates the inputs and states with a delay simultaneously. We consider an example to illustrate the developed results.