کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5000079 | 1460637 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hâ control of linear discrete-time systems: Off-policy reinforcement learning
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
In this paper, a model-free solution to the Hâ control of linear discrete-time systems is presented. The proposed approach employs off-policy reinforcement learning (RL) to solve the game algebraic Riccati equation online using measured data along the system trajectories. Like existing model-free RL algorithms, no knowledge of the system dynamics is required. However, the proposed method has two main advantages. First, the disturbance input does not need to be adjusted in a specific manner. This makes it more practical as the disturbance cannot be specified in most real-world applications. Second, there is no bias as a result of adding a probing noise to the control input to maintain persistence of excitation (PE) condition. Consequently, the convergence of the proposed algorithm is not affected by probing noise. An example of the Hâ control for an F-16 aircraft is given. It is seen that the convergence of the new off-policy RL algorithm is insensitive to probing noise.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automatica - Volume 78, April 2017, Pages 144-152
Journal: Automatica - Volume 78, April 2017, Pages 144-152
نویسندگان
Bahare Kiumarsi, Frank L. Lewis, Zhong-Ping Jiang,