Jingmei Liu

ORCID: 0000-0003-1938-7930
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About
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Research Areas
  • Advanced Control Systems Optimization
  • Stochastic processes and financial applications
  • Mathematical and Theoretical Epidemiology and Ecology Models
  • Game Theory and Applications
  • Fault Detection and Control Systems
  • Adaptive Dynamic Programming Control
  • Economic theories and models
  • Distributed Sensor Networks and Detection Algorithms
  • Insurance, Mortality, Demography, Risk Management
  • Advanced Queuing Theory Analysis
  • Random Matrices and Applications

Shandong University
2020-2024

Linyi University
2024

In this paper, we consider the discrete-time stochastic LQ optimal control with both initial and terminal constraints. The main contribution includes two aspects: one is to provide a necessary sufficient condition for exact reachability of systems; other characterize solvability constrained problem based on systems obtain explicitly controller. key technique innovatively transform system governed by forward difference equation into backward equation. This way, are able solve equations...

10.1109/tac.2024.3376422 article EN IEEE Transactions on Automatic Control 2024-09-01

10.1007/s12555-022-0534-5 article EN International Journal of Control Automation and Systems 2023-07-28

10.62678/ijics202409.10132 article EN International Journal of Intelligent Control and Systems 2024-09-01

Abstract In this paper, we study the team optimal decentralized estimation with partial history sharing information structure. There exist agents that have their own observations and share to each other. The main contributions include two aspects: One is give iterative equations of common estimation, which conditional expectation state respect for all innovation local agent; other provide structure is, linear combination information. novelty lies in can be obtained at one time by defining an...

10.1002/asjc.3168 article EN Asian Journal of Control 2023-06-19

This paper is concerned with the linear quadratic optimal control of discrete-time time-varying system terminal state constraint. The main contribution to propose a Q-learning algorithm for controller when matrices and input are both unknown. Different from existing algorithms in literature which mainly unconstrained problem, novelty proposed available deal case constraints. A numerical example illustrated verify effectiveness algorithm.

10.48550/arxiv.2307.09719 preprint EN other-oa arXiv (Cornell University) 2023-01-01
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