Title: Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design
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| Authors: Wang, D; He, HB; Liu, DR
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| Author Full Names: Wang, Ding; He, Haibo; Liu, Derong
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| Source: IEEE TRANSACTIONS ON CYBERNETICS, 47 (10):3417-3428; SI 10.1109/TCYB.2017.2653800 OCT 2017
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| Language: English
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| Abstract: In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H-infinity state feedback control design. First of all, the H-infinity control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.
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| ISSN: 2168-2267
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| eISSN: 2168-2275
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| IDS Number: FF9BM
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| Unique ID: WOS:000409311800038
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