This paper presents a framework for online adaptive optimal control of continuous-time linear systems with unknown dynamics. The approach uses approximate and adaptive dynamic programming to learn the optimal control policy and value function in real-time, without prior knowledge of the system matrices. We introduce two algorithms based on policy iteration and value iteration, providing proofs the convergence and stability. Our value iteration method is robust against from exploration noise. The effectiveness of these control strategies is demonstrated through two examples, highlighting their ability to achieve near-optimal performance despite unknown dynamics.
Pouyanfar, H. , Effati, S. and Mansoori, A. (2025). Dynamic adaptation strategies for optimal control in unknown linear time-invariant system. Journal of Mathematical Modeling, (), 1-18. doi: 10.22124/jmm.2025.30264.2716
MLA
Pouyanfar, H. , , Effati, S. , and Mansoori, A. . "Dynamic adaptation strategies for optimal control in unknown linear time-invariant system", Journal of Mathematical Modeling, , , 2025, 1-18. doi: 10.22124/jmm.2025.30264.2716
HARVARD
Pouyanfar, H., Effati, S., Mansoori, A. (2025). 'Dynamic adaptation strategies for optimal control in unknown linear time-invariant system', Journal of Mathematical Modeling, (), pp. 1-18. doi: 10.22124/jmm.2025.30264.2716
CHICAGO
H. Pouyanfar , S. Effati and A. Mansoori, "Dynamic adaptation strategies for optimal control in unknown linear time-invariant system," Journal of Mathematical Modeling, (2025): 1-18, doi: 10.22124/jmm.2025.30264.2716
VANCOUVER
Pouyanfar, H., Effati, S., Mansoori, A. Dynamic adaptation strategies for optimal control in unknown linear time-invariant system. Journal of Mathematical Modeling, 2025; (): 1-18. doi: 10.22124/jmm.2025.30264.2716
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