KMS Chongqing Institute of Green and Intelligent Technology, CAS
| A dynamic neural controller for adaptive optimal control of permanent magnet DC motors | |
Zhang, Yinyan1; Li, Shuai1; Luo, Xin2 ; Shang, Ming-Sheng2
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| 2017 | |
| 摘要 | The speed control of permanent magnet brushed (PMB) DC motors at low speeds is difficult due to the nonlinearity caused by various types of frictions. Under parameter uncertainty, the speed control becomes more difficult. In this paper, to handle the parameter uncertainty, we propose a dynamic neural network to adaptively reconstruct or learn the dynamics of PMB DC motors. Then, based on the parameters of the neural dynamic model, a near-optimal dynamic neural controller is designed and proposed for the speed control of PMB DC motors with frictions considered under parameter uncertainty. Simulations substantiate the efficacy of the proposed dynamic neural model and adaptive near-optimal controller for PMB DC motors with fully unknown parameters. © 2017 IEEE. |
| 语种 | 英语 |
| DOI | 10.1109/IJCNN.2017.7965939 |
| 会议(录)名称 | 2017 International Joint Conference on Neural Networks, IJCNN 2017
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| 页码 | 839-844 |
| 收录类别 | EI |
| 会议地点 | Anchorage, AK, United states |
| 会议日期 | May 14, 2017 - May 19, 2017 |
