摘要
在复杂非线性摩擦阻尼参数以及模型参数不确定的情况下,针对柔性关节机器人的状态观测问题,提出一种基于神经网络的无模型非线性观测器。采用径向基神经网络(RBF)对系统模型进行在线逼近,通过Lyapunov稳定性分析推导,获得神经网络权值自适应律;通过引入鲁棒项来抑制神经网络逼近误差,加快观测误差的收敛速度;通过不同激励下的仿真分析,验证了提出方法的有效性。
For the flexible joint manipulator state observer, proposes a model-free nonlinear observer based on neural network in the situation of uncertain model parameters and complex nonlinear friction damping parameter. By radial basis function neural network online approximation, the the neural network weight adaptive law are deduced by Lyapunov stability theory; Through introducing the robust terms to suppress the neural approximation error, accelerates the conver- gence rate of state observing error; Simulation analysis with different excitation verifies the effectiveness of the proposed method.
出处
《湖南工业大学学报》
2015年第3期35-40,共6页
Journal of Hunan University of Technology
