Self-Triggered Approximate Optimal Neuro-Control for Nonlinear Systems Through Adaptive Dynamic Programming.

In this article, a novel self-triggered approximate optimal neuro-control scheme is presented for nonlinear systems by utilizing adaptive dynamic programming (ADP). According to the Bellman principle of optimality, the cost function of the general nonlinear system is approximated by building a critic neural network with a nested updating weight vector. Thus, the Hamilton-Jacobi-Bellman equation is solved to indirectly obtain the approximate optimal neuro-control input.

Relaxed Optimal Control With Self-Learning Horizon for Discrete-Time Stochastic Dynamics.

The innovation of optimal learning control methods is profoundly propelled due to the improvement of the learning ability. In this article, we investigate the synthesis of initialization and acceleration for optimal learning control algorithms. This approach contrasts with traditional methods that concentrate solely on either the improvement of initialization or acceleration.

Integral Reinforcement Learning-Based Dynamic Event-Triggered Nonzero-Sum Games of USVs.

In this article, an integral reinforcement learning (IRL) method is developed for dynamic event-triggered nonzero-sum (NZS) games to achieve the Nash equilibrium of unmanned surface vehicles (USVs) with state and input constraints. Initially, a mapping function is designed to map the state and control of the USV into a safe environment. Subsequently, IRL-based coupled Hamilton-Jacobi equations, which avoid dependence on system dynamics, are derived to solve the Nash equilibrium.

A Hybrid Adaptive Dynamic Programming for Optimal Tracking Control of USVs.

This article presents an efficient method for solving the optimal tracking control policy of unmanned surface vehicles (USVs) using a hybrid adaptive dynamic programming (ADP) approach. This approach integrates data-driven integral reinforcement learning (IRL) and dynamic event-driven (DED) mechanisms into the solution of the control policy of the established augmented system while obtaining both the feedforward and feedback components of the tracking controller. For the USV model and the reference trajectory, an augmented system is established, and the tracking Hamilton-Jacobi-Bellman (HJB) equation is derived based on IRL, aiming to fully utilize system data information and reduce model dependency.

Zinc Single-Atom Catalysts Encapsulated in Hierarchical Porous Bio-Carbon Synergistically Enhances Fast Iodine Conversion and Efficient Polyiodide Confinement for Zn-I Batteries.

Aqueous zinc iodine (Zn-I) batteries have attracted attention due to their low cost, environmental compatibility, and high specific capacity. However, their development is hindered by the severe shuttle effect of polyiodides and the slow redox conversion kinetics of the iodine (I) cathode. Herein, a long-life Zn-I battery is developed by anchoring iodine within an edible fungus slag-derived carbon matrix encapsulated with Zn single-atom catalysts (SAZn@C).

Adaptive Tracking Control for Underactuated Double Pendulum Overhead Cranes With Variable Cable Length.

Although the literature on control of overhead crane systems is extensive and relatively mature, there is still a need to develop strategies that can simultaneously handle factors such as the double pendulum effect, variable cable length, input saturation, input dead zones, and external disturbances. This article is concerned with adaptive tracking control for underactuated overhead cranes in the presence of the above-mentioned challenging effects. The proposed controller is composed of the following two components.

Single-cell and genome-wide Mendelian randomization identifies causative genes for gout.

Background: Gout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis.

Methods: The study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout.

ROS-responsive nanoparticle delivery of ferroptosis inhibitor prodrug to facilitate mesenchymal stem cell-mediated spinal cord injury repair.

Spinal cord injury (SCI) is a traumatic condition that results in impaired motor and sensory function. Ferroptosis is one of the main causes of neural cell death and loss of neurological function in the spinal cord, and ferroptosis inhibitors are effective in reducing inflammation and repairing SCI. Although human umbilical cord mesenchymal stem cells (Huc-MSCs) can ameliorate inflammatory microenvironments and promote neural regeneration in SCI, their efficacy is greatly limited by the local microenvironment after SCI.

Optimal Learning Output Tracking Control: A Model-Free Policy Optimization Method With Convergence Analysis.

Optimal learning output tracking control (OLOTC) in a model-free manner has received increasing attention in both the intelligent control and the reinforcement learning (RL) communities. Although the model-free tracking control has been achieved via off-policy learning and Q-learning, another popular RL idea of direct policy learning, with its easy-to-implement feature, is still rarely considered. To fill this gap, this article aims to develop a novel model-free policy optimization (PO) algorithm to achieve the OLOTC for unknown linear discrete-time (DT) systems.

Epigenetic regulation and factors that influence the effect of iPSCs-derived neural stem/progenitor cells (NS/PCs) in the treatment of spinal cord injury.

Spinal cord injury (SCI) is a severe neurological disorder that causes neurological impairment and disability. Neural stem/progenitor cells (NS/PCs) derived from induced pluripotent stem cells (iPSCs) represent a promising cell therapy strategy for spinal cord regeneration and repair. However, iPSC-derived NS/PCs face many challenges and issues in SCI therapy; one of the most significant challenges is epigenetic regulation and that factors that influence this mechanism.

In Situ Synthesis of NH-MIL-53-Al/PAN Nanofibers for Removal Co(II) through an Electrospinning Process.

In this study, researchers developed a novel composite material called NH-MIL-53-Al/PAN, which consists of metal-organic frameworks (MOFs) grown on electrospun PAN nanofibers (NFs). The successful formation of the composite was confirmed by X-ray diffraction (XRD) and Fourier transform infrared (FTIR), and the hydrophilicity of NH-MIL-53-Al/PAN was demonstrated by the water contact angle (WCA). Batch experiments were conducted to investigate the adsorption performance of Co(II) under different conditions.

Adaptive dynamic programming-based hierarchical decision-making of non-affine systems.

In this paper, the problem of multiplayer hierarchical decision-making problem for non-affine systems is solved by adaptive dynamic programming. Firstly, the control dynamics are obtained according to the theory of dynamic feedback and combined with the original system dynamics to construct the affine augmented system. Thus, the non-affine multiplayer system is transformed into a general affine form.

Synchronization of Delayed Memristor-Based Neural Networks via Pinning Control With Local Information.

In this article, a novel pinning control method, only requiring information from partial nodes, is developed to synchronize drive-response memristor-based neural networks (MNNs) with time delay. An improved mathematical model of MNNs is established to describe the dynamic behaviors of MNNs accurately. In the existing literature, pinning controllers for synchronization of drive-response systems were designed based on information of all nodes, but in some specific situations, the control gains may be very large and challenging to realize in practice.

Rapid Adaptation for Active Pantograph Control in High-Speed Railway via Deep Meta Reinforcement Learning.

Active pantograph control is the most promising technique for reducing contact force (CF) fluctuation and improving the train's current collection quality. Existing solutions, however, suffer from two significant limitations: 1) they are incapable of dealing with the various pantograph types, catenary line operating conditions, changing operating speeds, and contingencies well and 2) it is challenging to implement in practical systems due to the lack of rapid adaptability to a new pantograph-catenary system (PCS) operating conditions and environmental disturbances. In this work, we alleviate these problems by developing a revolutionary context-based deep meta-reinforcement learning (CB-DMRL) algorithm.

Event-Triggered Robust Adaptive Dynamic Programming for Multiplayer Stackelberg-Nash Games of Uncertain Nonlinear Systems.

In this article, an event-triggered robust adaptive dynamic programming (ETRADP) algorithm is developed to solve a class of multiplayer Stackelberg-Nash games (MSNGs) for uncertain nonlinear continuous-time systems. Considering the different roles of players in the MSNG, the hierarchical decision-making process is described as the designed value functions for the leader and all followers, which assist to transform the robust control problem of the uncertain nonlinear system into an optimal regulation problem of the nominal system. Then, an online policy iteration algorithm is formulated to solve the derived coupled Hamilton-Jacobi equation.

Novel Discounted Adaptive Critic Control Designs With Accelerated Learning Formulation.

Inspired by the successive relaxation method, a novel discounted iterative adaptive dynamic programming framework is developed, in which the iterative value function sequence possesses an adjustable convergence rate. The different convergence properties of the value function sequence and the stability of the closed-loop systems under the new discounted value iteration (VI) are investigated. Based on the properties of the given VI scheme, an accelerated learning algorithm with convergence guarantee is presented.

Neuro-Optimal Event-Triggered Impulsive Control for Stochastic Systems via ADP.

This article presents a novel neural-network-based optimal event-triggered impulsive control method. First, a novel general-event-based impulsive transition matrix (GITM) is constructed to represent the probability distribution evolving characteristics regarding all system states across the impulsive actions, rather than the prefixed timing sequence. On the foundation of this GITM, the event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and its high-efficiency version (HEIADP) are developed to deal with the optimization problems for stochastic systems with event-triggered impulsive controls.

Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems.

This paper addresses decentralized tracking control (DTC) problems for input constrained unknown nonlinear interconnected systems via event-triggered adaptive dynamic programming. To reconstruct the system dynamics, a neural-network-based local observer is established by using local input-output data and the desired trajectories of all other subsystems. By employing a nonquadratic value function, the DTC problem of the input constrained nonlinear interconnected system is transformed into an optimal control problem.

Distributed Fault Tolerant Consensus Control of Nonlinear Multiagent Systems via Adaptive Dynamic Programming.

This article develops a distributed fault-tolerant consensus control (DFTCC) approach for multiagent systems by using adaptive dynamic programming. By establishing a local fault observer, the potential actuator faults of each agent are estimated. Subsequently, the DFTCC problem is transformed into an optimal consensus control problem by designing a novel local value function for each agent which contains the estimated fault, the consensus errors, and the control laws of the local agent and its neighbors.

N6-methyladenosine modification: A potential regulatory mechanism in spinal cord injury.

N6-methyladenosine (m6A), an essential post-transcriptional modification in eukaryotes, is closely related to the development of pathological processes in neurological diseases. Notably, spinal cord injury (SCI) is a serious traumatic disease of the central nervous system, with a complex pathological mechanism which is still not completely understood. Recent studies have found that m6A modification levels are changed after SCI, and m6A-related regulators are involved in the changes of the local spinal cord microenvironment after injury.

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning.

The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis.

Adaptive Dynamic Programming-Based Event-Triggered Robust Control for Multiplayer Nonzero-Sum Games With Unknown Dynamics.

In this article, the event-triggered robust control of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated by using adaptive dynamic programming. To relax the requirement of system dynamics, a neural network-based identifier is constructed by using the system input-output data. Subsequently, by designing a nonquadratic value function, which contains the bounded functions, the system states, and the control inputs of all players, the event-triggered robust stabilization problem is converted into an event-triggered constrained optimal control problem.

Adaptive Dynamic Programming-Based Cooperative Motion/Force Control for Modular Reconfigurable Manipulators: A Joint Task Assignment Approach.

This article develops a cooperative motion/force control (CMFC) scheme based on adaptive dynamic programming (ADP) for modular reconfigurable manipulators (MRMs) with the joint task assignment approach. By separating terms depending on local variables only, the dynamic model of the entire MRM system can be regarded as a set of joint modules interconnected by coupling torque. In addition, the Jacobian matrix, which reflects the interaction force of the MRM end-effector, can be mapped into each joint.