中国机械工程

• 机械基础工程 • 上一篇    下一篇

不确定轮式移动机器人统一自适应神经网络H∞控制

叶锦华;吴海彬   

  1. 福州大学机械工程及自动化学院,福州,350116
  • 出版日期:2017-01-25 发布日期:2017-01-20
  • 基金资助:
    国家自然科学基金资助项目(51175084);
    福建省自然科学基金资助项目(2015J05121);
    福州大学科研启动基金资助项目(510078);
    福州大学科技发展基金资助项目(650053)

Unified Adaptive Neural Network H∞ Control of Uncertain Wheeled Mobile Robots

YE Jinhua;WU Haibin   

  1. School of Mechanical Engineering and Automation, Fuzhou University,Fuzhou,350116
  • Online:2017-01-25 Published:2017-01-20

摘要: 提出了一种基于自适应神经网络控制和H∞控制的轮式移动机器人光滑全局跟踪和镇定统一的控制器。首先采用横截函数方法,扩展系统控制输入,建立与原系统等价的、输入输出完全解耦的无奇异全驱动系统,再对新系统设计自适应神经网络H∞控制器。自适应神经网络控制可有效补偿系统的复杂不确定项。H∞控制器可同时对系统扰动和神经网络逼近误差进行预定水平抑制,进一步提高控制器的适应性,优化系统的控制性能。仿真结果验证了算法的有效性。

关键词: 轮式移动机器人;轨迹跟踪与镇定统一控制;自适应神经网络;H&infin, 控制;横截函数

Abstract: A smooth global unified controller of trajectory tracking and stabilization was proposed for nonholomomic wheeled mobile robots based on adaptive neural network control and H∞ control. Firstly, the system control inputs were expanded by transverse function method, a nonsingular full drive system which was equivalent to original system was established with decoupled input-output. Then an adaptive neural network H∞ controller was designed for the new system, such that the complex system uncertainty was compensated effectively by the adaptive neural network. Disturbances and approximation errors were attenuated with a prescribed disturbance lever by the H∞ control. Adaptability of the controller were further improved, and the control performance was optimized. The effectiveness of the algorithm were verified by simulation results.

Key words: wheeled mobile robot, unified control of trajectory tracking and stabilization, adaptive neural network, H&infin, control, transverse function

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