基于RBF神经网络的鲁棒滑模观测器设计

针对非线性不确定性系统,提出一种鲁棒滑模观测器。所提出的鲁棒滑模观测器通过滑模与相应的控制策略来实现。设计参数的选取不需要求解大量方程,同时能保证对系统的非线性不确定性具有鲁棒性,系统中不确定性的上界值采用RBF神经网络进行自适应学习。通过设计滑模,可以调整观测器跟踪系统状态的收敛速度,使状态估计达到预期的指标。仿真结果验证了提出方法的有效性。

Design of robust sliding mode observer based on RBF neural network

A robust sliding mode observer for the nonlinearities or uncertainties of systems is proposed.The sliding mode manifold and control methodology are proposed.The design of the observer's parameters needs not to solve a lot of equations.The proposed observer is robust to the nonlinearities or uncertainties of systems.An adaptive RBF neural network is then used to learn the upper bound of system uncertainties.The convergence rate between the observer and the system can be changed by choosing suitable sliding mode manifold,so as to attain the desired performances.Simulation results are presented to validate the design.