Robust Adaptive Tracking Control for Nonlinear Systems Based on Self-Organizing Fuzzy CMAC Neural Networks
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摘要: 基于自组织模糊CMAC(SOFCMAC)神经网络,提出了一种非线性模型参考神经网络 增广逆系统鲁棒自适应跟踪控制方法.该方法的特点是通过S0FCMAC神经网络在线修正由 于建模误差、不确定因素等引起的非线性系统逆误差,使得系统输出准确跟踪参考模型输出. SOFCMAC的权值调整规律由Lyapunov稳定性理论导出.文中证明了非线性闭环系统的稳定 性.仿真例子表明了本文方法的有效性.Abstract: This paper presents a method of robust adaptive tracking control for nonlinear systems based on a self-organizing fuzzy CMAC neural network. The on-line learning while controlling neural network is used to adaptively regulate the error in the plant inversion which may be due to modeling uncertainties and disturbances in order to make the system outputs accurately track the outputs of the reference model. The updating rule of SOFCMAC weights is derived from Lyapunov stability theory. The stability of the designed system is proved. Simulation results demonstrate the effectiveness of the proposed method.
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Key words:
- Dynamic inversion /
- nonlinear /
- CMAC /
- adaptive control
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