PID Control of Obstacle-Breaking Weapon Based on Fuzzy Neural Network

TAO Zhengyong; TONG Zhongzhi; HOU Yuanlong; SHI Shang; HU Jinzhu

doi:10.3969/j.issn.1671-637x.2020.09.020

Journals >Electronics Optics & Control >Volume 27 >Issue 9 >Page 99 > Article

Electronics Optics & Control
Vol. 27, Issue 9, 99 (2020)

PID Control of Obstacle-Breaking Weapon Based on Fuzzy Neural Network

TAO Zhengyong, TONG Zhongzhi, HOU Yuanlong, SHI Shang, and HU Jinzhu

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[in Chinese]

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DOI: 10.3969/j.issn.1671-637x.2020.09.020 Cite this Article

TAO Zhengyong, TONG Zhongzhi, HOU Yuanlong, SHI Shang, HU Jinzhu. PID Control of Obstacle-Breaking Weapon Based on Fuzzy Neural Network[J]. Electronics Optics & Control, 2020, 27(9): 99 Copy Citation Text

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References

[3] SALEH A, SHAABAN A, RASHA T. The design and implementation of a fuzzy gain-scheduled PID controller for the Festo MPS PA compact workstation liquid level control［J］. Elsevier B. V. , 2020, 23(2):307-315.

CLP Journals

[1] TANG Dong, GAO Qiang, HOU Yuanlong, SHI Difen, ZHOU Shenglong, LIU Yuqi. RRNN-Based Sliding Mode Control of Obstacle Breaking Weapon[J]. Electronics Optics & Control, 2022, 29(1): 37

TAO Zhengyong, TONG Zhongzhi, HOU Yuanlong, SHI Shang, HU Jinzhu. PID Control of Obstacle-Breaking Weapon Based on Fuzzy Neural Network[J]. Electronics Optics & Control, 2020, 27(9): 99

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