In this study, a delay‐dependent memory state‐feedback controller for linear systems with input time‐delay in the presence of external disturbance has been investigated. At first, a new formula is introduced to obtain the prediction vector from the system dynamics and then by using this formula, the effect of input time‐delay on the original delayed system is reduced. To guarantee the prescribed disturbance attenuation level of the closed‐loop system, Lyapunov theory and linear matrix inequality (LMI) approaches are used. In the case of feasibility, sufficient LMI conditions provide the stabilising gain of the predictor‐based controller. To illustrate the effectiveness of the proposed method, it is applied to a quarter‐car model of an active suspension system considering the actuator time‐delay.

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

In this article, a decentralized scalable voltage control approach for islanded dc microgrids with a general structure is proposed. The microgrid system under study comprises several distributed generation (DG) units, every one containing an uncertain local ZIP [constant impedance (Z), constant current (I), and constant power (P)] load. We propose a voltage control strategy that ensures robust desired performance of the closed-loop islanded dc microgrid in addition to the robust stability under different uncertainty sources, including uncertain ZIP loads, topological changes, and plug-and-play functionality of DGs. The presented approach does not impose any restrictions on the dynamics of the distribution lines connecting different DG units. In the proposed control scheme, each local controller is the solution of a single convex optimization problem, which leads to the optimal performance of the dc microgrid. At first, an LTI state-space model with polytopic uncertainty is developed for islanded dc microgrid with different uncertainty sources. Then, a robust dynamic output-feedback-based controller with ${{\bm{H}}_\infty }$ performance criterion is proposed for the satisfaction of all control objectives. The final obtained problem is turned into an LMI-based optimization problem. Different scenarios are simulated in MATLAB to affirm the feasibility and efficiency of the proposed control technique in the dc microgrids.