This paper studies the zero-error tracking control problem of Euler-Lagrange systems subject to full-state constraints and nonparametric uncertainties. By blending an error transformation with barrier Lyapunov function, a neural adaptive scheme is developed, resulting in solution several salient features: 1) action continuous smooth; 2) converges prescribed compact set around origin within given finite time at controllable rate convergence that can be uniformly prespecified; 3) Nussbaum gain...

This paper investigates the position and attitude tracking control problem of a quadrotor unmanned aerial vehicle subject to modeling uncertainties actuator failures. A comprehensive mathematical model reflecting nonlinearity state-space coupling dynamics as well actuation faults external disturbances is derived. By combining radial basis function neural networks (NNs) with virtual parameter estimating algorithms, an indirect NN-based adaptive fault-tolerant scheme developed, which exhibits...

Abstract Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) vital for ensuring engineering safety risk management. This study proposes an innovative IHO-VMD-CatBoost model that integrates variational mode decomposition (VMD) the CatBoost algorithm, with hyperparameters globally optimized using improved hippocampus optimization algorithm (IHO). Compared to existing models, proposed method improves feature...

In this paper, we present a neuroadaptive control for class of uncertain nonlinear strict-feedback systems with full-state constraints and unknown actuation characteristics where the break points dead-zone model are considered as time-variant. order to deal modeling uncertainties impact nonsmooth characteristics, neural networks utilized at each step backstepping design. By using barrier Lyapunov function, together concept virtual parameter, develop scheme ensuring tracking stability same...

Steering-by-wire (SBW) systems can enhance driving experience and improve autonomous performance. A key challenge is simultaneously meeting the steering angle tracking requirements for both manual modes when dealing with inconsistent command update cycles, lack of speed sensors, unmodeled dynamics, nonlinear disturbances. This paper adopts a hierarchical cascade control structure comprising an loop velocity loop, focusing on improving performance by optimizing angular controller. The...

Dengue virus (DENV) is one of the major arboviruses that pose a serious threat to global human health. However, there currently no specific antiviral drug available for treatment DENV infection. DDX17, member DExD/H-box helicase family, has been implicated in replication processes various viruses. Our research group discovered during early stages dengue replication, DDX17 promotes viral and suppresses activity IFN promoter. Furthermore, binds dsRNA interacts with G3BP1, component stress...

Abstract N6-methyladenosine (m6A) is present in diverse viral RNA and plays important regulatory roles virus replication host antiviral innate immunity. However, the role of m6A regulating JEV has not been investigated. Here, we show that genome contains modification upon infection mouse neuroblast cells (neuro2a). results a decrease expression writer METTL3 brain tissue. knockdown by siRNA leads to substantial production progeny viruses at 48 hpi. Mechanically, triggered considerable...

To enhance the control accuracy of lane-keeping assistance systems for trucks encountering crosswind-induced lateral deviations to improve stability vehicle, this study proposes a strategy based on linear quadratic regulator (LQR) using path-tracking preview model. First, deviation is calculated Then, an observer vehicle’s sideslip angle designed vehicle tracking model and Kalman filter controller, used solve angle. Finally, feedforward controller LQR two-degrees-of-freedom eliminate...

This article proposes a neural networks (NNs)-based tracking control approach for class of uncertain high-order self-restructuring nonaffine dynamic systems. Unlike most existing NN-based works that normally ignore the precondition on functionality and reliability NN unit thus can only ensure semiglobal stability, proposed method explicitly addresses issue reliable in-loop operation approximation-based unit, resulting in safeguarded solution capable ensuring globally stable tracking....

In this paper, we investigate the optimal power allocation strategy in joint spectrum overlay and underlay cognitive radio network where a licensee multiple unlicensed users coexist operate same spectrum. We propose an scheme which achieves maximum system utility while satisfying QoS requirement of SUs interference constrains PU. Particularly, since adopting do not necessarily have perfect sensing knowledge regarding licensee, modify with imperfect knowledge. Moreover, sensitivity based...

In many data mining tasks, there is a large supply of unlabeled but limited labeled since it expensive generated. Therefore, number semi-supervised clustering algorithms have been proposed, few them are specially designed for multi-type relational data. this paper, k-means algorithm which based on the combination method and clustering. order to achieve high performance, in algorithm, we first analyze all kinds relationships data, include intra-relationship, inter-relationship, explicit...

Improving the steering angle tracking performance of steer-by-wire (SBW) system is crucial to vehicle maneuverability and driving safety. However, due complex disturbances caused by velocity-dependent tire-road contact characteristics, lagged sensor signals unmodeled dynamics, enhancing robustness, improving bandwidth minimizing overshoot pose significant challenges in controller scheme. Compared with conventional cascade angle-speed-current control scheme, robust strategy proposed achieve...

Insulated gate bipolar transistors (IGBTs) are capable of efficiently and stably converting regulating electrical power. Precise evaluation the aging degree IGBTs is particularly important. This study proposes a data-driven approach for IGBT based on multi-observation sequence particle filtering support vector regression. method effectively integrates data from different devices, significantly reducing uncertainty, constructs an model small amount data. A series experiments has verified...

Parkinson’s disease is a neurodegenerative that seriously affects the quality of life patients. In this study, we propose new diagnosis method using deep learning techniques. The takes multi-channel sensor signals as inputs, and full convolutional LSTM blocks model perceive same time-series inputs from two different views, connect extracted spatial features with temporal features. order to improve detection performance, channel attention mechanism was incorporated into model, data...

This paper presents a low-cost control for class of high-order nonlinear multi-input multi-output (MIMO) systems in the presence actuation faults. The proposed bears PI form with analytical algorithms gains auto-tuning. resultant action is continuous and able to ensure uniformly ultimately boundedness all signals closed-loop system. salient feature also lies its low complexity computation effectiveness dealing modeling uncertainties nonlinearities as well