IEEE Trans Neural Netw Learn Syst. 2015 Feb;26(2):224-36. doi: 10.1109/TNNLS.2014.2312001.
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.
我们针对一类具有多输入多输出的不确定非线性系统，提出了一种自适应输出反馈动态面控制（DSC）方法，能够保持规定的性能。通过设计神经网络观测器和修改 DSC 方法，实现了多个控制目标。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声状态网络（ESN）观测器来获取在线系统状态，从而估计传统状态反馈控制中不可测量的状态，并通过 ESN 逼近未知函数。然后，通过引入高阶滑模微分器来替代每个步骤中的一阶滤波器，开发了一种改进的 DSC 方法，从而降低了滤波器性能对闭环稳定性的影响。此外，输入到状态稳定性保证了整个闭环系统的所有信号都是半全局一致最终有界的。具体来说，性能函数使跟踪误差收敛到平衡点附近的一个紧致集。两个数值示例说明了所提出的控制方案，结果令人满意。
