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首页> 外文期刊>Mathematical Problems in Engineering >Adaptive NN Control for Multisteering Plane Aircraft with Dead Zone or Backlash Input Nonlinearity
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Adaptive NN Control for Multisteering Plane Aircraft with Dead Zone or Backlash Input Nonlinearity

机译：具有死区或反冲输入非线性的多舵飞机的自适应神经网络控制

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摘要

Considering that many factors such as actuator input dead zone, backlash, and external disturbance could affect the exactness of trajectory tracking, therewith a robust adaptive neural network control scheme on the basis of control allocation is proposed for the sake of tracking control of multisteering plane aircraft with actuator input dead zone or backlash nonlinearity. First of all, an actuator input dead zone or backlash nonlinearity control assignment model is established and the control allocation equation is derived. Secondly, the system nonlinear uncertainty is compensated by means of radial basis function neural network, and a robust term is introduced to achieve robustness against external disturbance and system errors. Finally, by utilizing Lyapunov stability theorem, it has been proved that all the signals in the closed-loop system are bounded, and the tracking error converges to a small residual set asymptotically. Simulation results on ICE101 multisteering plane aircraft demonstrate the outstanding tracking performance and strong robustness as well as effectiveness of the proposed approach, which can effectively overcome the adverse influence of dead zone, backlash nonlinearity, and external disturbance on the system.
机译：考虑到执行器输入死区，反冲和外部干扰等多种因素会影响轨迹跟踪的准确性，提出一种基于控制分配的鲁棒自适应神经网络控制方案，以实现多舵飞机的跟踪控制。执行器输入死区或齿隙非线性。首先，建立执行器输入死区或齿隙非线性控制分配模型，并推导控制分配方程。其次，利用径向基函数神经网络对系统的非线性不确定性进行补偿，并引入了鲁棒性项来达到抵御外界干扰和系统误差的鲁棒性。最后，利用李雅普诺夫稳定性定理，证明了闭环系统中的所有信号都是有界的，跟踪误差渐近收敛到一个小的残差集。在ICE101多舵飞机上的仿真结果表明，该方法具有出色的跟踪性能，强大的鲁棒性和有效性，可以有效克服死区，反冲非线性和外部干扰对系统的不利影响。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第6期|4684303.1-4684303.8|共8页
  • 作者

    Meng Xiang-fei; Wang Ying; Lv Mao-long;

  • 作者单位

    Air Force Engn Univ, Equipment Management & Safety Engn Coll, Xian 710051, Peoples R China;

    Air Force Engn Univ, Equipment Management & Safety Engn Coll, Xian 710051, Peoples R China;

    Air Force Engn Univ, Equipment Management & Safety Engn Coll, Xian 710051, Peoples R China;

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