On stabilizability and exact observability of stochastic systems with their applications
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Abstract (arXiv)
This paper discusses the stabilizability, weak stabilizability, exact observability and robust quadratic stabilizability of linear stochastic control systems. By means of the spectrum technique of the generalized Lyapunov operator, a necessary and sufficient condition is given for stabilizability and weak stabilizability of stochastic systems, respectively. Some new concepts called unremovable spectrums, strong solutions, and weakly feedback stabilizing solutions are introduced. An unremovable spectrum theorem is given, which generalizes the corresponding theorem of deterministic systems to stochastic systems. A stochastic Popov-Belevith-Hautus (PBH) criterion for exact observability is obtained. For applications, we give a comparison theorem for generalized algebraic Riccati equations (GAREs), and two results on Lyapunov-type equations are obtained, which improve the previous works. Finally, we also discuss robust quadratic stabilization of uncertain stochastic systems, and a necessary and sufficient condition is given for quadratic stabilization via a linear matrix inequality (LMI).
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jul 2023.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 1.
5 papers.
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- 2025Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noisecited by 0
- 2026Stabilizer Design for Policy Iteration in Stochastic Linear Quadratic Control: A Spectrum-Assignment Approachcited by 0
- 2026Stabilizer Design for Policy Iteration in Stochastic Linear Quadratic Control: A Spectrum-Assignment Approach6 Aug 2026
- 2025Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noise3 Jun 2025
- 2025Robust Mean Field Social Control: A Unified Reinforcement Learning Framework27 Feb 2025
- 2024Mean Field LQG Social Optimization: A Reinforcement Learning Approach19 Oct 2024
- 2024Model-free H∞ control of Ito stochastic system via off-policy reinforcement learning7 Mar 2024