基于RBF的飞行模拟器离散终端滑模控制

Y. Chen, Weihong Wang
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引用次数: 2

摘要

为了实现飞行模拟器伺服系统的高跟踪精度,提出了一种将RBF神经网络(RBFNN)与离散滑模控制(DSMC)相结合的控制方案。该控制器采用基于李雅普诺夫稳定定理的RBF神经网络估计SMC的开关增益,从而减轻了摩擦和开关抖振的影响。通过在离散SMC推导中加入终端函数实现终端滑模。当滑动面为非线性时,系统状态可在有限时间内达到平衡点。该方法可以有效地减小滑模控制中滑模表面周围的抖振。仿真结果验证了所提控制器的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RBF based discrete terminal sliding mode control for flight simulator
A control scheme combining RBF Neural Network (RBFNN) and Discrete Sliding Mode Control (DSMC) is proposed in this paper to achieve high tracking precision for flight simulator servo systems. In this controller, the switching gain of SMC is estimated by RBF Neural Network based on Lyapunov stable theorem, thus the effect of friction and on switch chattering can be alleviated. Moreover, terminal sliding mode is realized by adding terminal function in discrete SMC derivation. With the nonlinear sliding surfaces, system states can reach the equilibrium point in a finite time period. The chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed controller.
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