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首页> 外文期刊>International journal of systems,control and communications >A novel terminal sliding mode observer with RBF neural network for a class of nonlinear systems
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A novel terminal sliding mode observer with RBF neural network for a class of nonlinear systems

机译：带有RBF神经网络的新型终端滑模观测器用于一类非线性系统

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摘要

A novel scheme for designing a new observer with combining radial basis function neural network (RBFNN) and terminal sliding mode approaches is presented. Terminal sliding mode adopted to cover the effects of internal disturbances of the system and neural network handles the problem of uncertainties and unmodelled dynamics. Convergence of the observer error to zero and accurate estimation of uncertainties of the nonlinear system are the main advantages of the proposed observer. This observer is designed based on output injection method in which the error is injected to the next state in every step until it reaches the last state of the system. Eventually, the error is suppressed and converged to zero in the last state by applying RBFNN. The stability of neural network weights which are updated adaptively and the error dynamic are guaranteed by the Lyapunov theory. Finally, the simulation result shows the promising performances of the proposed observer.
机译：提出了一种结合径向基函数神经网络（RBFNN）和终端滑模方法设计新观测器的新方案。采用终端滑动模式来覆盖系统内部扰动和神经网络的影响，解决了不确定性和动力学未建模的问题。观测器误差收敛到零并精确估计非线性系统的不确定性是所提出的观测器的主要优点。该观察器是基于输出注入方法设计的，在该方法中，错误会在每个步骤中注入到下一个状态，直到到达系统的最后状态。最终，通过应用RBFNN，该错误被抑制并在最后一个状态下收敛为零。 Lyapunov理论保证了自适应更新的神经网络权重的稳定性和误差动态性。最后，仿真结果显示了提出的观测器的有希望的性能。

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