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A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control
by
Wang, Qi
, Wang, Min
, Yu, Haitao
, Qi, Xinbo
in
adaptive dynamic programming (ADP)
/ Control systems
/ Digital signal processors
/ goal representation heuristic dynamic programming (GrHDP)
/ permanent magnet synchronous motor (PMSM)
/ reinforcement learning (RL)
/ single artificial neuron (SAN)
/ single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP)
2018
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A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control
by
Wang, Qi
, Wang, Min
, Yu, Haitao
, Qi, Xinbo
in
adaptive dynamic programming (ADP)
/ Control systems
/ Digital signal processors
/ goal representation heuristic dynamic programming (GrHDP)
/ permanent magnet synchronous motor (PMSM)
/ reinforcement learning (RL)
/ single artificial neuron (SAN)
/ single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP)
2018
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Do you wish to request the book?
A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control
by
Wang, Qi
, Wang, Min
, Yu, Haitao
, Qi, Xinbo
in
adaptive dynamic programming (ADP)
/ Control systems
/ Digital signal processors
/ goal representation heuristic dynamic programming (GrHDP)
/ permanent magnet synchronous motor (PMSM)
/ reinforcement learning (RL)
/ single artificial neuron (SAN)
/ single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP)
2018
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A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control
Journal Article
A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control
2018
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Overview
A speed controller for permanent magnet synchronous motors (PMSMs) under the field oriented control (FOC) method is discussed in this paper. First, a novel adaptive neuro-control approach, single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP) for speed regulation of PMSMs, is presented. For both current loops, PI controllers are adopted, respectively. Compared with the conventional single artificial neuron (SAN) control strategy, the proposed approach assumes an unknown mathematic model of the PMSM and guides the selection value of parameter K online. Besides, the proposed design can develop an internal reinforcement learning signal to guide the dynamic optimal control of the PMSM in the process. Finally, nonlinear optimal control simulations and experiments on the speed regulation of a PMSM are implemented in Matlab2016a and TMS320F28335, a 32-bit floating-point digital signal processor (DSP), respectively. To achieve a comparative study, the conventional SAN and SAN-GrHDP approaches are set up under identical conditions and parameters. Simulation and experiment results verify that the proposed controller can improve the speed control performance of PMSMs.
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