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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Modified Neural Dynamic Surface Approach to Output Feedback of MIMO Nonlinear Systems
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Modified Neural Dynamic Surface Approach to Output Feedback of MIMO Nonlinear Systems

机译：MIMO神经网络输出反馈的改进神经动态面法。

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摘要

We report an adaptive output feedback dynamic surface control (DSC), maintaining the prescribed performance, for a class of uncertain nonlinear systems with multiinput and multioutput. Designing neural network observers and modifying the DSC method achieves several control objectives. First, to achieve output feedback control, the finite-time echo state networks (ESN) observer with fast convergence is designed to obtain the online system states. Thus, the immeasurable states in traditional state feedback control are estimated and the unknown functions are approximated by ESN. Then, a modified DSC approach is developed by introducing a high-order sliding mode differentiator to replace the first-order filter in each step. Thus, the effect of filter performance on closed-loop stability is reduced. Furthermore, the input to state stability guarantees that all signals of the whole closed-loop system are semiglobally uniformly ultimately bounded. Specifically, the performance functions make the tracking errors converge to a compact set around equilibrium. Two numerical examples illustrated the proposed control scheme with satisfactory results.
机译：我们报告了一类具有多输入多输出的不确定非线性系统的自适应输出反馈动态表面控制（DSC），保持规定的性能。设计神经网络观察者并修改DSC方法可实现多个控制目标。首先，为了实现输出反馈控制，设计了具有快速收敛性的有限时间回波状态网络（ESN）观察器来获取在线系统状态。因此，估计了传统状态反馈控制中的不可估量状态，并通过ESN估算了未知函数。然后，通过引入高阶滑模微分器以替换每一步中的一阶滤波器，来开发改进的DSC方法。因此，减少了滤波器性能对闭环稳定性的影响。此外，状态稳定性的输入保证了整个闭环系统的所有信号最终在半全局统一范围内。具体而言，性能函数使跟踪误差收敛到围绕平衡的紧凑集。两个数值例子说明了所提出的控制方案，结果令人满意。

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