Beijing Institute of Mathematical Sciences and Applications Beijing Institute of Mathematical Sciences and Applications

  • About
    • President
    • Governance
    • Partner Institutions
    • Visit
  • People
    • Management
    • Faculty
    • Postdocs
    • Visiting Scholars
    • Staff
  • Research
    • Research Groups
    • Courses
    • Seminars
  • Join Us
    • Faculty
    • Postdocs
    • Students
  • Events
    • Conferences
    • Workshops
    • Forum
  • Life @ BIMSA
    • Accommodation
    • Transportation
    • Facilities
    • Tour
  • News
    • News
    • Announcement
    • Downloads
About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > Seminar on Control Theory and Nonlinear Filtering Datatic Approximate Optimal Filter for Nonlinear Systems with Non-Gaussian Noises
Datatic Approximate Optimal Filter for Nonlinear Systems with Non-Gaussian Noises
Organizer
Shing Toung Yau
Speaker
Weixian He
Time
Wednesday, June 26, 2024 4:00 PM - 5:00 PM
Venue
理科楼A-304
Abstract
State estimation presents a critical challenge across diverse engineering domains, particularly when dealing with the nonlinear and non-Gaussian characteristics in complex control systems. Conventional approaches often resort to approximations like model linearization or Monte Carlo sampling, which may compromise precision or encounter significant computational overhead. This paper proposes a data-driven offline estimation method called datatic approximate optimal filter (DAOF), which is tailored for nonlinear systems under non-Gaussian conditions. Due to its structural flexibility, this method allows for both model-based and model-free estimation, depending on the availability of the state-space model. To obtain DAOF, we formulate the reinforced estimation problem (REP), where the optimal state estimate is computed to minimize accumulated estimation error, establishing a connection with reinforcement learning (RL). We design a model-based filter similar to Kalman filter (KF) for nonlinear and non-Gaussian systems, incorporating prediction and update components. We further design the filtering structure for model-free estimation by directly choosing the state estimate as the policy output. An actor-critic learning algorithm is employed to obtain a parameterized filtering policy for both filtering structures. We integrate a sliding window on the input of the policy, enabling the retention of historical observation information while maintaining high online computational efficiency. Experimental results on the 2-DOF nonlinear vehicle system and the Lorenz system showcases the superior accuracy and computational efficiency of DAOF compared to representative nonlinear filters. The model-free estimation capability of DAOF is validated with a 14-DOF vehicle dynamic model without explicitly providing the transition or observation functions.
Beijing Institute of Mathematical Sciences and Applications
CONTACT

No. 544, Hefangkou Village Huaibei Town, Huairou District Beijing 101408

北京市怀柔区 河防口村544号
北京雁栖湖应用数学研究院 101408

Tel. 010-60661855
Email. administration@bimsa.cn

Copyright © Beijing Institute of Mathematical Sciences and Applications

京ICP备2022029550号-1

京公网安备11011602001060 京公网安备11011602001060