Robust Predictive Strategy Research Articles

Disordered Rock-Salt Type Li2TiS3 as Novel Cathode for LIBs: A Computational Point of View

The development of high-energy cathode materials for lithium-ion batteries with low content of critical raw materials, such as cobalt and nickel, plays a key role in the progress of lithium-ion batteries technology. In recent works, a novel and promising family of lithium-rich sulfides has received attention. Among the possible structures and arrangement, cubic disordered Li2TiS3 has shown interesting properties, also for the formulation of new cell for all-solid-state batteries. In this work, a computational approach based on DFT hybrid Hamiltonian, localized basis functions and the use of the periodic CRYSTAL code, has been set up. The main goal of the present study is to determine accurate structural, electronic, and spectroscopic properties for this class of materials. Li2TiS3 precursors as Li2S, TiS2, and TiS3 alongside other formulations and structures such as LiTiS2 and monoclinic Li2TiS3 have been selected as benchmark systems and used to build up a consistent and robust predictive scheme. Raman spectra, XRD patterns, electronic band structures, and density of states have been simulated and compared to available literature data. Disordered rock-salt type Li2TiS3 structures have been derived via a solid solution method as implemented into the CRYSTAL code. Representative structures were extensively characterized through the calculations of their electronic and vibrational properties. Furthermore, the correlation between structure and Raman fingerprint was established.

Robust Exact Predictive Scheme for Output-Feedback Control of Input-Delay Systems With Unmatched Sinusoidal Disturbances

Predictor-based control has been widely used to compensate input delays. However, the conventional predictors such as Smith predictor, usually have poor robustness with respect to system disturbances. In this article, a novel robust predictive scheme, that can achieve exact state prediction in finite-time, is proposed for linear time-invariant (LTI) systems subject to input delay and unmatched sinusoidal disturbances. By combining the super-twisting technique with the proposed predictive scheme, we also propose a new predictor-based output-feedback super-twisting controller which can compensate arbitrary long input delay and eliminate the effect of unmatched disturbances from the corresponding state completely. The simulation comparison shows the effectiveness of the theoretical results.

Delay-Tolerant Adaptive Robust Tracking Control of Uncertain Time-Delay Systems

The simultaneous presence of parameter variations, time-varying disturbances, and delay in the control loop of an $n{\text{th}}$ -order (possibly unstable) dynamical system makes formal tracking control design difficult. Existing model-based control methodologies can handle only a subset of the above-mentioned complexities, and hence, become insufficient to accurately track the desired trajectory. In this paper, a prediction-based adaptive robust control framework is proposed for high performance control of dynamical systems subject to the above-mentioned complexities, which involves the following. First, prediction-based projection-type adaptation laws with model compensation to reduce the effect of parameter uncertainties under delay and time-varying disturbances. Second, a robust prediction scheme that factors in both unknown parameters and disturbance uncertainties under delay to handle the resulting unmatched disturbances. Third, prediction-based continuous robust feedback to attenuate the cumulative effect of disturbance terms due to uncertain prediction. The controller guarantees semiglobal, exponential convergence of the tracking error with an ultimate error bound proportional to delay, disturbance, and controller gain. The controller effectiveness is demonstrated with an illustrative flight control example and compared with the baseline adaptive robust controller.

Robust predictive scheme for input delay systems subject to nonlinear disturbances

The predictor-based control is known as an effective method to compensate input delays. Yet the traditional predictors, like Smith predictor, have poor robustness with respect to system disturbances. In this paper, with the consideration of future disturbances, a novel robust predictive scheme is developed for input delay systems subject to nonlinear disturbances. The Artstein reduction method is used to provide performance analysis of different predictor-based controllers, which shows that the proposed predictor-based controller can provide better disturbance attenuation than previous approaches in the literature for a wide range of disturbances.

A New Scheme for Daily Peak Wind Gust Prediction Using Machine Learning

A major challenge in meteorology is the forecasting of winds owing to their highly chaotic nature. However, wind forecasts, and in particular daily peak wind gust forecasts, provide the public with a general sense of the risks associated with wind on a given day and are useful in decision making. Additionally, such knowledge is critical for wind energy production. Currently, no operational daily peak wind gust product exists. As such, this project will seek to develop a peak wind gust prediction scheme based on output from an operational numerical weather prediction model. Output from the North American Mesoscale (NAM) model will be used in a support vector regression (SVR) algorithm trained to predict daily peak wind gusts for ten cities commonly impacted by hazardous wind gusts (cities in the Midwest and central Plains) and with interests in wind energy. Output from a kernel principal component analysis of the fully three-dimensional atmosphere as characterized by the NAM forecasts will be used to predict peak wind gusts for each location at 36hours lead time. Ultimately, this initial product will lead to the development of a more robust prediction scheme that could one day transition into an operational forecast model.