TSMC

Pan Tang, Fubiao Zhang, Jianchuan Ye, D. Lin

Abstract This paper proposes a finite-time fault-tolerant trajectory tracking control for a quadrotor system against external disturbances, parametric uncertainties and actuator faults without the need of fault diagnosis mechanism. First, a finite-time fault-tolerant control based on a multivariable integral terminal sliding mode control (TSMC) is proposed to separately accommodate the parametric uncertainties and actuator faults. Then, combined with an adaptive law, an adaptive fault-tolerant control (AFTC) is employed to eliminate the requirement of the prior knowledge on the uncertainty bounds and achieve high-precision trajectory tracking in a finite time. Besides, a finite-time exact observer (FEO) is incorporated to estimate and compensate for the disturbances in a finite time while keeping the control gains of AFTC to small values. The promising features of the proposed fault-tolerant control scheme include finite-time convergence and insensitivity to multiple faults and disturbances. The finite-time stability of the closed-loop system is proved by Lyapunov theory. Finally, the effectiveness of the proposed method is validated through extensive numerical simulations for a variety of working scenarios.

Peng Li, Xiang Yu, Youmin Zhang, Xiaoyan Peng

S. Moore

Two of the world's largest foundries—Taiwan Semiconductor Manufacturing Co. (TSMC) and Samsung—announced in April that they'd climbed one more rung on the Moore's Law ladder. TSMC spoke first, saying its 5-nanometer manufacturing process is now in what's called “risk production”—the company believes it has finished the process, but initial customers are taking a chance that it will work for their designs. Samsung followed quickly with a similar announcement.

F. Lamzouri, E. Boufounas, A. Brahmi, A. Amrani

Abstract In the present paper, we report a robust and efficient terminal sliding mode controller based particle swarm optimization (PSOTSMC) for maximum power point tracking (MPPT) of photovoltaic (PV) system under variable atmospheric conditions. The PSO-TSMC controller combines the features of both terminal sliding mode control (TSMC) and particle swarm optimization (PSO) method. The proposed approach is designed based TSMC controller as robust nonlinear controller in order to make the PV system performs at the desired reference maximum power voltage (MPV) despite the atmospheric conditions variation, by regulating the control duty cycle. Moreover, the proposed approach applied TSMC controller with their optimal parameter by using PSO method. Furthermore, a comparative study, including the proposed PSO-TSMC controller, the standard TSMC and the conventional sliding mode control (SMC), is investigated under variable atmospheric conditions. Hence, simulation results reveal that the proposed approach assures more robustness against atmospheric conditions variation with best tracking performance and fast tracking response convergence in finite time compared to the other controllers (i.e. TSMC and SMC).

M. Chowdhury, Timothy E. Rice, M. Oehlschlaeger

The identification of gas mixture speciation from a complex multicomponent absorption spectrum is a problem in gas sensing that can be addressed using machine-learning approaches. Here, we report on a deep convolutional neural network for multigas classification using terahertz (THz) absorption spectra, THz spectra mixture classifier network or TSMC-Net. TSMC-Net has been developed to identify eight volatile organic compounds in mixtures based on their fingerprint rotational absorption spectra in the 220-330 GHz frequency range. A data set consisting of simulated absorption spectra for randomly generated mixtures, with absorption greater than thresholds representing detectable limits and annotated with multiple labels, was prepared for model development. The supervised multilabel classification problem, i.e., the identification of individual gases in a mixture, is converted to a supervised multiclass classification problem via label powerset conversion. The trained model is validated and tested against simulated spectra for gas mixtures, with and without white Gaussian noise. The trained model exhibits high precision, recall, and accuracy for each pure compound. Class activation maps illustrate the complex decision-making process of the model and highlight relevant frequency regions that are needed to identify unique mixtures. Finally, the model was demonstrated against measured THz absorption spectra for pure species and mixtures, acquired using a microelectronics-based THz absorption spectrometer. The data set generation strategy and deep convolutional neural network approach are generalized and can be extrapolated to other spectroscopy types, frequency ranges, and sensors.