Dynamic Compensator-Based Output Feedback Controller Design for Uncertain Systems with Adjustable Robustness

A low-order dynamics compensator-based output feedback stabilization method for a class of uncertain linear MIMO systems with mismatched disturbances is presented. Since all the system states are not measurable, the proposed controller inherently has low-pass filter property in which it can successfully replace the derivative terms of the system output and hence effectively estimate the input disturbance. The control scheme proposed here can simultaneously consider the input saturation problem and obtain the desired performance. Although the system has unknown uncertainties and disturbances, the uniformly ultimate boundedness of system states in the closed-loop system is analytically shown using the Lyapunov method. Finally, two numerical examples are presented to demonstrate the applicability of the proposed scheme.

Empirical Mode Decomposition of AE Signal Processing Based on Hilbert-Huang Transformation

In this paper a new signal processing method of Hilbert-Huang transform is applied to analyze AE lead-breaking signals and loaded concrete AE signals. Empirical model of decomposition (EMD) and cubic interpolation were used in AE signals analysis which was based on the local characteristic time scale. After the decomposition inherent model function (IMF) can effectively preserve non-linear and non-stationary features of signals during the processing. Meanwhile, noises of signals were eliminated by using multi-dimension filter property of EMD. The above two signal’s IMF can be clearly shown on a time-frequency vs. energy distribution spectrum and marginal spectrum by Hilbert-Huang transformation. The results show that HHT can efficiently reflecting the intrinsic properties of signals. Besides, HHT method, which is more adaptive than other methods, had a bigger superiority in adaptability and frequency concentration, and it has batter localization property and visual result of the time-frequency domain.