Neural Network Adaptive Iterative Learning Control for Strict-Feedback Unknown Delay Systems Against Input Saturation.

Neural network adaptive iterative learning control (ILC) is developed in this article to treat strict-feedback nonlinear systems with unknown state delays and input saturation. These delays are treated by constructing the Lyapunov-Krasovskii (L-K) functions for each subsystem. A command filter is employed to avoid the derivative explosion caused by continuous differentiation of the virtual controller.

Data-driven set-point learning control with ESO and RBFNN for nonlinear batch processes subject to nonrepetitive uncertainties.

This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction.

LQR approach to robust stabilization of state space systems with matched uncertainties.

This note shows an elegant relationship between the quadratic optimal control and robust stabilization for linear time-invariant (LTI) systems, where the former control can robustly stabilize the latter system, provided that the matched uncertainty is bounded. Through reviewing the relevant literature, some common mistakes in regard to this relationship are found. The correct results are obtained and proved in both frequency and time domains.

Qinzhizhudan formula dampens inflammation in microglia polarization of vascular dementia rats by blocking MyD88/NF-κB signaling pathway: Through integrating network pharmacology and experimental validation.

Ethnopharmacological Relevance: Qinzhizhudan Formula (QZZD) is composed of Scutellaria baicalensis Georgi (Huang Qin) extract, Gardenia jasminoides (Zhizi) extract and Suis Fellis Pulvis (Zhudanfen) (ratio of 4:5:6). This formula is optimized from Qingkailing (QKL) injection. Regarding brain injury, QZZD is protective.

Emerging effects of non-coding RNA in vascular endothelial cells during strokes.

Vascular and neurological damage are the typical outcomes of ischemic strokes. Vascular endothelial cells (VECs), a substantial component of the blood-brain barrier (BBB), are necessary for normal cerebrovascular physiology. During an ischemic stroke (IS), changes in the brain endothelium can lead to a BBB rupture, inflammation, and vasogenic brain edema, and VECs are essential for neurotrophic effects and angiogenesis.

Optimization and Developmental Validation of 38-plex InDel Panel for Ancestry Inference.

Objectives: The previously established 38-plex InDel system was optimized and its performance was validated according to the Scientific Working Group on DNA Analysis Method (SWGDAM) application guidelines. The ancestry inference accuracy of individuals from East Asian, European, African and mixed populations was verified.

Methods: DNA standard sample 9947A was used as the template to establish the optimal amplification conditions by adjusting primer balance, Mg final concentration and optimizing PCR thermal cycle parameters and amplification volume.

Event-Triggered Adaptive Neural Network Tracking Control for Uncertain Systems With Unknown Input Saturation Based on Command Filters.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem.

Event-Triggered Adaptive Fuzzy Neural Network Output Feedback Control for Constrained Stochastic Nonlinear Systems.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors.

Temporal alterations in pericytes at the acute phase of ischemia/reperfusion in the mouse brain.

Pericytes, as the mural cells surrounding the microvasculature, play a critical role in the regulation of microcirculation; however, how these cells respond to ischemic stroke remains unclear. To determine the temporal alterations in pericytes after ischemia/reperfusion, we used the 1-hour middle cerebral artery occlusion model, which was examined at 2, 12, and 24 hours after reperfusion. Our results showed that in the reperfused regions, the cerebral blood flow decreased and the infarct volume increased with time.

Identification of tRNA-derived Fragments and Their Potential Roles in Atherosclerosis.

Objective: Atherosclerosis (AS), a chronic inflammatory disease, is the basis of cardiovascular disease (CVD). Although the treatment has been greatly improved, AS still imposes a large burden on human health and the medical system, and we still need to further study its pathogenesis. As a novel biomolecule, transfer RNA-derived fragments (tRFs) play a key role in the progression of various disease.

Hierarchical Passivity Criterion for Delayed Neural Networks via A General Delay-Product-Type Lyapunov-Krasovskii Functional.

This article is concerned with passivity analysis of neural networks with a time-varying delay. Several techniques in the domain are improved to establish the new passivity criterion with less conservatism. First, a Lyapunov-Krasovskii functional (LKF) is constructed with two general delay-product-type terms which contain any chosen degree of polynomials in time-varying delay.

A survey on attack detection, estimation and control of industrial cyber-physical systems.

Cyber-physical systems (CPSs) are complex systems that involve technologies such as control, communication, and computing. Nowadays, CPSs have a wide range of applications in smart cities, smart grids, smart manufacturing and intelligent transportation. However, with integration of industrial control systems with modern communication technologies, CPSs would be inevitably exposed to increasing security threats, which could lead to severe degradation of the system performance and even destruction of CPSs.

Analysis and prediction of COVID-19 epidemic in South Africa.

The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively.

Stability Analysis for Delayed Neural Networks via a Novel Negative-Definiteness Determination Method.

The stability of neural networks with a time-varying delay is studied in this article. First, a relaxed Lyapunov-Krasovskii functional (LKF) is presented, in which the positive-definiteness requirement of the augmented quadratic term and the delay-product-type terms are set free, and two double integral states are augmented into the single integral terms at the same time. Second, a new negative-definiteness determination method is put forward for quadratic functions by utilizing Taylor's formula and the interval-decomposition approach.

Adaptive control of a class of strict feedback nonlinear systems under replay attacks.

The adaptive control of a class of strict-feedback nonlinear system under replay attack is investigated in this paper. Durations of each attack and the resting time after each attack are analyzed and their explicit bounds are presented to ensure closed-loop stability. Two scenarios are considered.

Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis.

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed.

The immune status of patients with psoriasis vulgaris of blood-stasis syndrome and blood-dryness syndrome: a qualitative evidence synthesis.

Background: Chinese medicine (CM) classifies psoriasis vulgaris into three syndromes: blood-heat syndrome (BHS), blood-stasis syndrome (BSS), and blood-dryness syndrome (BDS). The levels of several immunological serum markers in BHS have been established. We aimed to investigate the immune status of patients with psoriasis vulgaris of BSS and BDS.

Polynomial Lyapunov Functions for Synchronization of Nonlinearly Coupled Complex Networks.

In this article, we search for polynomial Lyapunov functions beyond the quadratic form to investigate the synchronization problems of nonlinearly coupled complex networks. First, with a relaxed assumption than the quadratic condition, a synchronization criterion is established for nonlinearly coupled networks with asymmetric coupling matrices. Compared with the existing synchronization criteria, our results are less conservative and have a wider application.

Finite-Time Tracking Control for Nonlinear Systems via Adaptive Neural Output Feedback and Command Filtered Backstepping.

This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is proposed. The neural network models the unknown nonlinear dynamics, the finite-time command filter (FTCF) guarantees the approximation of its output to the derivative of virtual control signal in finite time at the backstepping procedure, and the fraction power-based error compensation system compensates for the filtering errors between FTCF and virtual signal.

Adaptive Finite-Time Containment Control of Uncertain Multiple Manipulator Systems.

This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping.

Design, analysis and application of a new disturbance rejection PID for uncertain systems.

In this paper, a new disturbance rejection proportional-integral-derivative (DR-PID) scheme is proposed for a class of minimum phase plants with low relative order. The essential active disturbance rejection (ADR) mechanism that is otherwise hidden in PID control structure has been illuminated and clarified in this paper for the first time.The proposed DR-PID scheme is derived on the basis of a modified disturbance observer to embed the active disturbance rejection mechanism seamlessly in the classical PID structure.

Reachable Set Estimation for Discrete-Time Markovian Jump Neural Networks With Generally Incomplete Transition Probabilities.

This paper is concerned with the problem of reachable set estimation for discrete-time Markovian jump neural networks with generally incomplete transition probabilities (TPs). This kind of TP may be exactly known, merely known with lower and upper bounds, or unknown. The aim of this paper is to derive a precise reachable set description for the considered system via the Lyapunov-Krasovskii functional (LKF) approach.

Hyodeoxycholic acid protects the neurovascular unit against oxygen-glucose deprivation and reoxygenation-induced injury .

Calculus bovis is commonly used for the treatment of stroke in traditional Chinese medicine. Hyodeoxycholic acid (HDCA) is a bioactive compound extracted from calculus bovis. When combined with cholic acid, baicalin and jas-minoidin, HDCA prevents hypoxia-reoxygenation-induced brain injury by suppressing endoplasmic reticulum stress-mediated apoptotic signaling.