Nonlinear model predictive control-Cross-coupling control with deep neural network feedforward for multi-hydraulic system synchronization control.

This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm.

Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.

Introduction: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.

Methods: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented.

Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study.

This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE).