Speaker
Description
Event-triggered state estimation has gained significant attention in resource-constrained environments such as network control system due to its ability to save communication and energy resources. For example, it has been observed that event-triggered Kalman filtering-based approaches can achieve resource efficiency in general nonlinear systems [1]. Also the idea of Moving Horizon Estimation (MHE) with its application in event-triggered settings is known to address state estimation tasks effectively [2]. The presented work investigates and compares different such event-triggered state estimation techniques for nonlinear systems, focusing on their performance under different mechanisms and triggering rules.
In this talk, we introduce the principles of event-triggered state estimation and focus on two types of triggering mechanisms: innovation-based and send-on-delta, combined with stochastic and deterministic triggering rules. We compare the common state estimation methods, including the Kalman filtering-based approaches: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF), as well as optimization-based MHE. Numerical results are presented for four nonlinear systems, including a two-link robot arm. Due to the limitations of event-triggered MHE for nonlinear systems, a linear system-based design is used for comparison.
The results demonstrate that CKF achieves the best performance in highly nonlinear systems, while EKF and MHE are more effective in linear or weakly nonlinear settings. Notably, an innovation-based mechanism consistently offers more appropriate triggering sample time, while a deterministic rule enhances the estimation performance. Despite its accuracy, MHE incurs the highest computational costs, followed by CKF, UKF, and EKF.
[1] Marzieh Kooshkbaghi, Horacio J Marquez, and Wilsun Xu. “Event-triggered approach to dynamic state estimation of a synchronous machine using cubature Kalman filter”. In: IEEE Transactions on Control Systems Technology 28.5 (2019), pp. 2013–2020.
[2] Xunyuan Yin and Jinfeng Liu. “Event-triggered state estimation of linear systems using moving horizon estimation”. In: IEEE Transactions on Control Systems Technology 29.2 (2020), pp. 901–909.
