Tuning the Solvation Structure in Water-Based Solution Enables Surface Reconstruction of Layered Oxide Cathodes toward Long Lifespan Sodium-Ion Batteries.

Layered oxides of sodium-ion batteries suffer from severe side reactions on the electrode/electrolyte interface, leading to fast capacity degradation. Although surface reconstruction strategies are widely used to solve the above issues, the utilization of the low-cost wet chemical method is extremely challenging for moisture-sensitive Na-based oxide materials. Here, the solvation tuning strategy is proposed to overcome the deterioration of NaNi1/3Mn1/3Fe1/3O2 in water-based solution and conduct the surface reconstruction. When capturing the water molecules by the solvation structure of cations, here is Li+, the structural collapse and degradation of layered oxides in water-based solvents are greatly mitigated. Furthermore, Li(H2O)3EA+ promotes the profitable Li+/Na+ exchange to build a robust surface, which hampers the decomposition of electrolytes and the structural evolution upon cycling. Accordingly, the lifespan of Li-reinforced materials is prolonged to three times that of the pristine one. This work represents a step forward in understanding the surface reconstruction operated in a water-based solution for high-performance sodium layered oxide cathodes.

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding.

The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.

Event-Based Nonsingular Fixed-Time Tracking Control of an Uncertain Manipulator System Subject to Full-State Static Constraints.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an n -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

A flexible current tracking control of sensorless induction motors via adaptive observer.

In this article, an interconnected adaptive observer-based backstepping control method is proposed to achieve sensorless current control without persistent excitation condition. Based on the stability theories of mathematics, an effective observer utilized to estimate the fluxes and external load is given. It has a good foundation for breaking the limitation caused by the coupling between the control process and the parameter estimation course. The characteristic of the uncoupling can make the control range for currents more wider. Due to the effective online tracking and the good adaptability of the designed observers, the control effect shows good robustness to the parameters. Finally, numerical simulations are provided to intuitively illustrate the effectiveness and the superiority of the theoretical results.

Robust adaptive antiswing control of underactuated crane systems with two parallel payloads and rail length constraint.

The antiswing control and accurate positioning are simultaneously investigated for underactuated crane systems in the presence of two parallel payloads on the trolley and rail length limitation. The equations of motion for the crane system in question are established via the Euler-Lagrange equation. An adaptive control strategy is proposed with the help of system energy function and energy shaping technique. Stability analysis shows that under the designed adaptive controller, the payload swings can be suppressed ultimately and the trolley can be regulated to the destination while not exceeding the pre-specified boundaries. Simulation results are provided to show the satisfactory control performances of the presented control method in terms of working efficiency as well as robustness with respect to external disturbances.

Finite-time stabilization of uncertain nonholonomic systems in feedforward-like form by output feedback.

This paper investigates the problem of finite-time stabilization by output feedback for a class of nonholonomic systems in chained form with uncertainties. Comparing with the existing relevant literature, a distinguishing feature of the systems under investigation is that the x-subsystem is a feedforward-like rather than feedback-like system. This renders the existing control methods inapplicable to the control problems of the systems. A constructive design procedure for output feedback control is given. The designed controller renders that the states of closed-loop system are regulated to zero in a finite time. Two simulation examples are provided to illustrate the effectiveness of the proposed approach.