Model-Based Proprioceptive State Estimation for Spring-Mass Running

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽

10.3390/s21062085 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2085

Author(s):

Xue-Bo Jin ◽

Ruben Jonhson Robert RobertJeremiah ◽

Ting-Li Su ◽

Yu-Ting Bai ◽

Jian-Lei Kong

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Future Development ◽

Data Driven ◽

Estimation Methods ◽

Multiple Model ◽

Development Trends ◽

Research Results ◽

Model Driven ◽

Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

MODEL‐BASED ANOMALY DETECTION FOR POWER SYSTEM STATE ESTIMATION

Advances in Electric Power and Energy ◽

10.1002/9781119480402.ch4 ◽

2020 ◽

pp. 99-121

Author(s):

Aditya Ashok ◽

Manimaran Govindarasu ◽

Venkataramana Ajjarapu

Keyword(s):

Anomaly Detection ◽

Power System ◽

State Estimation ◽

System State ◽

Power System State Estimation ◽

Model Based

An Online Model-based Battery Parameter and State Estimation Method Using Multi-scale Dual Adaptive Particle Filters

Energy Procedia ◽

10.1016/j.egypro.2017.03.976 ◽

2017 ◽

Vol 105 ◽

pp. 4549-4554 ◽

Cited By ~ 6

Author(s):

Min Ye ◽

Hui Guo ◽

Rui Xiong ◽

Hao Mu

Keyword(s):

State Estimation ◽

Particle Filters ◽

Estimation Method ◽

Multi Scale ◽

Model Based

A novel Gaussian model based battery state estimation approach: State-of-Energy

Applied Energy ◽

10.1016/j.apenergy.2015.04.062 ◽

2015 ◽

Vol 151 ◽

pp. 41-48 ◽

Cited By ~ 48

Author(s):

HongWen He ◽

YongZhi Zhang ◽

Rui Xiong ◽

Chun Wang

Keyword(s):

State Estimation ◽

Gaussian Model ◽

Model Based

Host–Target Vehicle Model-Based Lateral State Estimation for Preceding Target Vehicles Considering Measurement Delay

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2018.2828125 ◽

2018 ◽

Vol 14 (9) ◽

pp. 4190-4199 ◽

Cited By ~ 5

Author(s):

Yafei Wang ◽

Zhisong Zhou ◽

Chongfeng Wei ◽

Yahui Liu ◽

Chengliang Yin

Keyword(s):

State Estimation ◽

Vehicle Model ◽

Target Vehicle ◽

Model Based ◽

Measurement Delay ◽

Host Target