Phase Control on Surface for the Stabilization of High Energy Cathode Materials of Lithium Ion Batteries.

The development of high energy electrode materials for lithium ion batteries is challenged by their inherent instabilities, which become more aggravated as the energy densities continue to climb, accordingly causing increasing concerns on battery safety and reliability. Here, taking the high voltage cathode of LiNi0.5Mn1.5O4 as an example, we demonstrate a protocol to stabilize this cathode through a systematic phase modulating on its particle surface. We are able to transfer the spinel surface into a 30 nm shell composed of two functional phases including a rock-salt one and a layered one. The former is electrochemically inert for surface stabilization while the latter is designated to provide necessary electrochemical activity. The precise synthesis control enables us to tune the ratio of these two phases, and achieve an optimized balance between improved stability against structural degradation without sacrificing its capacity. This study highlights the critical importance of well-tailored surface phase property for the cathode stabilization of high energy lithium ion batteries

Adaptive fuzzy tracking control for a class of singular systems via output feedback scheme

The problem of adaptive fuzzy observer-based tracking control for nonlinear singular systems is considered in this paper. The nonlinear singular systems are composed of two kinds of subsystems, being called the differential subsystem and algebraic subsystem, which are coupled with each other. The systems can be non-strict feedback structures. Through designing a new state observer and a linear controller, an error system with a tunable parameter is obtained. By constructing new one-sided Lipschitz conditions, the regularization and impulse-free conditions are proposed for the error system. With the help of the tunable parameter in the observer, we design an output feedback controller for the nonlinear singular systems to ensure that all states of the closed-loop system are bounded and the tracking and observer errors remain in a neighborhood of the origin. Two examples are provided to illustrate the effectiveness of the presented method.</p

Command Filter-Based Finite-Time Constraint Control for Flexible Joint Robots Stochastic System with Unknown Dead Zones

This article studies the problem of finite-time (FT) adaptive constraint control for flexible joint robots (FJR) stochastic system. First, by combining the command filtered backstepping method with FT control, not only does it solve the 'explosion of complexity' problem, but it also ensures that the error of the FJR stochastic system converges in FT. Second, the asymmetric time-varying output constraint problem of FJR stochastic system is solved by designing a nonlinear transformation function (NTF) only depends on the system output, which reduces the difficulty of system stability analyses and relaxes the constraints on the initial value of the output. Third, by exploiting the fuzzy logic system, the adverse effect of the unknown stochastic nonlinear disturbances generated by the harmonic drive of the FJR system is effectively overcome. Furthermore, by utilizing the boundary information of dead-zone slopes, the adverse impact of the dead-zone inputs on the efficacy of control is effectively compensated. Finally, the Lyapunov approach is employed to indicate that the signals are convergent, and the simulation results demonstrate the effectiveness of the control algorithm.</p

Fuzzy Observer-based Command Filtered Adaptive Control of Flexible Joint Robots with Time-varying Output Constraints

Flexible joint robots (FJR) systems are used in many aspects of actual production due to its high compliance, low energy consumption, human-computer interaction safety and other characteristics. A fuzzy observer-based command filtered adaptive control method is applied to make FJR systems with time-varying output constraints (TVOC) and model uncertainties operate safely in a complex environment in this brief. Chiefly, a fuzzy observer is developed to estimate the link's angle velocity and motor angle velocity of the FJR. Next, by combining time-varying barrier Lyapunov function (TVBLF) with fuzzy logic systems, the uncertainties of the FJR model are approximated without violating the TVOC. Besides, the command filtered method with error compensation signal resolves the issue of 'explosion of complexity' and removes the impacts of filtering errors. The stability of the FJR system is verified by Lyapunov stability theory. Simulation shows that the devised approach can insure the TVOC, the validity of the observer and position tracking accuracy of the system.</p

Cross-lingual Alzheimer's Disease detection based on paralinguistic and pre-trained features

We present our submission to the ICASSP-SPGC-2023 ADReSS-M Challenge Task, which aims to investigate which acoustic features can be generalized and transferred across languages for Alzheimer's Disease (AD) prediction. The challenge consists of two tasks: one is to classify the speech of AD patients and healthy individuals, and the other is to infer Mini Mental State Examination (MMSE) score based on speech only. The difficulty is mainly embodied in the mismatch of the dataset, in which the training set is in English while the test set is in Greek. We extract paralinguistic features using openSmile toolkit and acoustic features using XLSR-53. In addition, we extract linguistic features after transcribing the speech into text. These features are used as indicators for AD detection in our method. Our method achieves an accuracy of 69.6% on the classification task and a root mean squared error (RMSE) of 4.788 on the regression task. The results show that our proposed method is expected to achieve automatic multilingual Alzheimer's Disease detection through spontaneous speech.Comment: accepted by ICASSP 202

Adaptive Fuzzy Finite-Time Singular Perturbation Control for Flexible Joint Manipulators With State Constraints

An adaptive fuzzy finite-time singular perturbation control is proposed for flexible joint manipulators with state constraints. First, the flexible joint manipulator system is decoupled into a rigid subsystem and a fast subsystem through singular perturbation technique. Second, a finite-time controller is introduced to improve the response speed of the rigid subsystem so that it can converge within a finite time. And then, all the rigid subsystem states are confined within the scope of the constraint by the barrier Lyapunov function. Third, the model’s uncertainties and unknown external disturbances are handled by adaptive fuzzy technique. Finally, the effectiveness of the new control scheme is illustrated by the simulation

Event-Triggered Adaptive Fuzzy Finite-Time Output Feedback Control for Stochastic Nonlinear Systems With Input and Output Constraints

This article focuses on the problem of designing an adaptive fuzzy event-triggered finite-time output feedback control for stochastic nonlinear systems with input and output constraints. A fuzzy observer is designed to estimate the unmeasured states. The quartic asymmetric time-varying barrier Lyapunov function is established to ensure constraint satisfaction. By utilizing the stochastic theory, finite-time command filtered backstepping method and event-triggered mechanism, a finite-time event-triggered controller is recursively designed, which can not only guarantee finite-time convergent property, but also reduce communication pressure. Meanwhile, the matter of 'explosion of complexity' is removed by introducing the finite-time command filter and the effect of filtered errors is offset by constructing error compensation signals. Moreover, an auxiliary system is introduced to handle the input constraint. Finally, the effectiveness of the theoretical results is demonstrated by the simulation example.</p