Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems
Bo PANG
Tao BIAN
Zhong-Ping JIANG
摘要：This paper studies data-driven learning-based methods for the finite-horizon optimal control of linear time-varying discrete-time systems. First, a novel finite-horizon Policy Iteration (PI) method for linear time-varying discrete-time systems is presented. Its connections with existing infinite-horizon PI methods are discussed. Then, both data-driven off-policy PI and Value Iteration (VI) algorithms are derived to find approximate optimal controllers when the system dynamics is completely unknown. Under mild conditions, the proposed data-driven off-policy algorithms converge to the optimal solution. Finally, the effectiveness and feasibility of the developed methods are validated by a practical example of spacecraft attitude control.
机标关键词：
资助基金：The work of B. Pang and Z.-P. Jiang has been supported in part by the National Science Foundation (ECCS-1501044)
论文发表日期：2019-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：12( 73-84 )
英文信息