A new control scheme for nonlinear systems with disturbances
A new control scheme for nonlinear systems with disturbances
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DOI：
10.1109/tcst.2005.860510
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发表时间：
2006
影响因子：
4.8
通讯作者：
Zenglian Liu;J. Svoboda
中科院分区：
文献类型：
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作者：
Zenglian Liu;J. Svoboda
针对一类带有未知扰动的时变非线性系统，提出了一种基于非线性扰动观测器（NDO）和带反馈误差学习（FEL）策略的滑模模糊神经网络（SFNN）耦合的学习控制方案.所提出的控制器，称为NDOFEL，包括两个步骤，用于获得时变集中干扰d（t）的估计，以提高跟踪控制的精度。NDO最初被应用于估计d（t），但是由于d/spl dot/（t）/spl ne/0，所以观测器误差不收敛到零。然后提出SFNN来估计观测器误差，使得系统的输出遵循期望的轨迹。所提出的NDOFEL具有稳定的在线学习能力，在存在干扰的情况下保持高的控制性能，并保证闭环系统的稳定性的基础上的李雅普诺夫定理。仿真结果表明，所提出的NDOFEL的有效性和鲁棒性在机翼摇摆现象的跟踪控制。结果表明，所提出的控制器可以显着提高飞机的跟踪性能。
A new learning control scheme, based on a nonlinear disturbance observer (NDO) coupled with a sliding-mode fuzzy neural network (SFNN) with a feedback-error-learning (FEL) strategy, is proposed for a class of time-varying nonlinear systems with unknown disturbances. The proposed controller, referred to as NDOFEL, involves two steps for obtaining an estimate of the time-varying lumped disturbance d(t) for improving the precision of the tracking control. The NDO is initially applied to estimate d(t), but an observer error does not converge to zero since d/spl dot/(t)/spl ne/0. The SFNN is then presented to estimate the observer error such that the output of systems follows a desired trajectory. The proposed NDOFEL has stable on-line learning ability, maintains high control performance in the presence of disturbance, and guarantees the stability of closed-loop systems on the basis of the Lyapunov theorem. The effectiveness and robustness of the proposed NDOFEL is demonstrated through simulation results obtained for the tracking control during wing rock phenomena. The results suggest that the proposed controller can significantly enhance the tracking performance of aircraft.