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| Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems |
| BoPANG,TaoBIAN,Zhong-PingJIANG |
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| (Control and Networks (CAN) Lab, Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, U.S.A.) |
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| DOI：https://doi.org/10.1007/s11768-019-8168-8 |
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| 基金项目:The work of B. Pang and Z.-P. Jiang has been supported in part by the National Science Foundation (No. ECCS-1501044). |
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| Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems |
| Bo PANG,Tao BIAN,Zhong-Ping JIANG |
| (Control and Networks (CAN) Lab, Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, U.S.A.;Bank of America Merrill Lynch, One Bryant Park, New York, NY 10036, U.S.A.) |
| Abstract: |
| 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. |
| Key words: Optimal control, time-varying system, adaptive dynamic programming, policy iteration (PI), value iteration (VI) |
