Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems

作者：Pang, Bo^*; Bian, Tao; Jiang, Zhong-Ping

来源：Control Theory and Technology, 2019, 17(1): 73-84.

DOI：10.1007/s11768-019-8168-8

• Adaptive dynamic programming
• Discrete
• time systems
• Finite horizon optimal control
• Learning-based methods
• Optimal controls
• Policy iteration
• Spacecraft attitude control
• Value iteration

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.

• 出版日期2019-2-1