采样非线性系统的神经网络稳定自适应控制
Stable Adaptive Control for Sampled-Data Nonlinear Systems Using Neural Networks
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摘要: 针对一类动力学未知或难以建模的采样非线性系统,提出了一种基于神经网络的跟随控 制器稳定自适应控制方法.控制器采用径向基函数神经网络近似对象的动力学非线性,神经 网络参数的自适应规律由稳定理论得到.文中给出了系统稳定性和跟随误差收敛性的证明, 并通过仿真实例揭示了所提方法的性能.Abstract: A neural network-based stable adaptive control approach is developed in this paper for a class of sampled-data nonlinear systems, for which the nonlinear system dynamics are either unknown or difficult to obtain. The controller employs Radial Basis Function (RBF) neural networks to adaptively compensate for the plant nonlinearities, and the neural network parameters are adapted using stability theory. A complete stability and tracking error convergence proof is given, and the effectiveness of the proposed control approach is illustrated through simulation studies of a two-link manipulator.
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Key words:
- Adaptive control /
- sampled-data nonlinear systems /
- neural networks /
- sliding mode
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