Reinforcement Learning-Based Adaptive Control of a Piezo-Driven Nanopositioning System
This article proposes a new reinforcement learning (RL)-based adaptive control design for precision motion control of a two-degree-of-freedom piezoelectric XY nanopositioning system. In this design, an actor-critic structure is developed to eliminate the effects of uncertain nonlinearities and cross...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10402007/ |
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| Summary: | This article proposes a new reinforcement learning (RL)-based adaptive control design for precision motion control of a two-degree-of-freedom piezoelectric XY nanopositioning system. In this design, an actor-critic structure is developed to eliminate the effects of uncertain nonlinearities and cross-coupling motion between the two working axes. Then, an adaptive parameter adjustment mechanism is designed to optimize the control performance without a priori knowledge of the unknown perturbations. The effectiveness and superiority of the proposed method are verified by performing simulation and experimental studies. The results show that the proposed RL-based adaptive control method provides a better robust performance and smaller tracking error for the nanopositioning system. |
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| ISSN: | 2644-1284 |