Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems |
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| Authors: | Bo PANG Tao BIAN Zhong-Ping JIANG |
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| Affiliation: | 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. and 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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| 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. |
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| Keywords: | Optimal control time-varying system adaptive dynamic programming policy iteration (PI) value iteration (VI) |
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