Barrier Lyapunov Functions-Based Adaptive Neural Control for Permanent Magnet Synchronous Motors With Full-State Constraints | Zendy

Yingying Liu | Zendy; Jinpeng Yu | Zendy; Haisheng Yu | Zendy; Chong Lin | Zendy; Lin Zhao | Zendy

Open Access

Barrier Lyapunov Functions-Based Adaptive Neural Control for Permanent Magnet Synchronous Motors With Full-State Constraints

Author(s) -

Yingying Liu,

Jinpeng Yu,

Haisheng Yu,

Chong Lin,

Lin Zhao

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2713419

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Considering the requirement of high accuracy and nonlinear problems in drive systems, a novel adaptive position tracking control approach based on neural networks is presented for permanent magnet synchronous motors with full-state constraints. The neural networks technique is employed to approximate the unknown nonlinear functions. Then, the barrier Lyapunov functions are used to restrict the state variables within a bounded compact set to improve the property of system. The proposed adaptive neural network controllers can guarantee that all closed-loop variables are bounded, and the full state variables do not exceed their constraint spaces. Simulation results show the effectiveness and the potentials of the theoretic results obtained.

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