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Dynamic adaptation strategies for optimal control in unknown linear time-invariant system | ||
| Journal of Mathematical Modeling | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 13 تیر 1404 اصل مقاله (409.45 K) | ||
| نوع مقاله: Special Issue: 7th National Seminar on Control and Optimization | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2025.30264.2716 | ||
| نویسندگان | ||
| Homa Pouyanfar؛ Sohrab Effati* ؛ Amin Mansoori | ||
| Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P. O. Box 1159, Mashhad 91775, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Optimal control؛ Adaptive dynamic programming؛ Policy iteration؛ Value iteration؛ Exploration noise | ||
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آمار تعداد مشاهده مقاله: 3 تعداد دریافت فایل اصل مقاله: 4 |
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