Reinforcement learning‐based adaptive optimal tracking algorithm for Markov jump systems with partial unknown dynamics
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Publication:6054459
DOI10.1002/oca.2903MaRDI QIDQ6054459
Kaibo Shi, Haiyang Fang, Hai Wang, Shuping He, Yidong Tu
Publication date: 25 October 2023
Published in: Optimal Control Applications and Methods (Search for Journal in Brave)
algebraic Riccati equationreinforcement learningpolicy iterationMarkov jump systemsadaptive optimal tracking
Learning and adaptive systems in artificial intelligence (68T05) Adaptive control/observation systems (93C40) Optimal stochastic control (93E20)
Cites Work
- Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics
- Fuzzy robust tracking control for uncertain nonlinear systems
- Observer-based adaptive sliding mode control for nonlinear Markovian jump systems
- Stability and stabilization of Markovian jump linear systems with partly unknown transition probabilities
- Adaptive optimal control for continuous-time linear systems based on policy iteration
- Online policy iterative-based \(H_\infty\) optimization algorithm for a class of nonlinear systems
- Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning
- Controllability, stabilizability, and continuous-time Markovian jump linear quadratic control
- Lyapunov iterations for optimal control of jump linear systems at steady state
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