During the measurement of friction force, the measured signal generally contains noise. To remove the noise and preserve the important features of the signal, a hybrid filtering method is introduced that uses the mutual information and a new waveform. This new waveform is the difference between the original signal and the sum of intrinsic mode functions (IMFs), which are obtained by empirical mode decomposition (EMD) or its improved versions. To evaluate the filter performance for the friction signal, ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), and complete ensemble EMD with adaptive noise (CEEMDAN) are employed in combination with the proposed filtering method. The combination is used to filter the synthesizing signals at first. For the filtering of the simulation signal, the filtering effect is compared under conditions of different ensemble number, sampling frequency, and the input signal-noise ratio, respectively. Results show that CEEMDAN outperforms other signal filtering methods. In particular, this method is successful in filtering the friction signal as evaluated by the de-trended fluctuation analysis (DFA) algorithm.

This study presents an adaptive sliding mode control method for DC–DC converters. In the state space model of DC–DC converters, there are always some unknown coordinate components of the desired equilibrium point (static working point), which are necessary to obtain the error vector and then build the sliding surface. To obtain the unknown coordinate components of the equilibrium point, an adaptive law is proposed. The adaptive law and the error response are formulated in a single equation, which determines the system performance. A systematic sliding mode control design procedure of a class of DC–DC converters is hence carried out. To verify the usefulness and effectiveness of the proposed adaptive sliding mode control method, the negative output elementary super lift Luo converter is used as a design example. Simulation and experimental results are reported to confirm the proposed approach.

A novel laser self-mixing interferometry vibration measurement algorithm based on the local maximum detection technique is proposed, that can reconstruct micro vibrations rapidly and easily. In this article, the principles of the local maximum detection algorithm are analyzed in detail, and the process of solving the window function is emphasized. The major advantage of the method is that it does not involve any complicated calculations and removes the need for optical/electromechanical components. The validity of the presented method is confirmed by means of simulated signals and demonstrated via several experiments for harmonic and aleatory motion.