Search

In this article, a novel adaptive self-triggered controller is constructed to handle the fault-tolerant control problem for nonlinear system with input dead zone through a funnel function method: 1) the actuator failure and the dead-zone nonlinearity are fully analyzed for the modeling purpose, we transform the system into a new model; 2) the self-triggered scheme is set up to update the control signals, which effectively economizes communication resources; 3) due to the existence of unknown control gains and unknown nonlinear functions, the adaptive neural network funnel controllers are designed, such that the tracking error is confined within the funnel boundary and all the signals of the closed-loop system are semi-globally uniformly ultimately bounded; and 4) finally, the applicability of the acquired control algorithm is demonstrated via a rigid robot manipulator system.

Background Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis.Methods To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis.Results A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9.Conclusion These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

Domain adaption (DA) is a challenging task that integrates knowledge from source domain (SD) to perform data analysis for target domain. Most of the existing DA approaches only focus on single-source-single-target setting. In contrast, multisource (MS) data collaborative utilization has been extensively used in various applications, while how to integrate DA with MS collaboration still faces great challenges. In this article, we propose a multilevel DA network (MDA-NET) for promoting information collaboration and cross-scene (CS) classification based on hyperspectral image (HSI) and light detection and ranging (LiDAR) data. In this framework, modality-related adapters are built, and then a mutual-aid classifier is used to aggregate all the discriminative information captured from different modalities for boosting CS classification performance. Experimental results on two cross-domain datasets show that the proposed method consistently provides better performance than other state-of-the-art DA approaches.

Identifying the Hamiltonian of an unknown quantum system is a critical task in the area of quantum information. In this article, we propose a systematic Hamiltonian identification approach via quantum ensemble multiclass classification (HI-QEMC). This approach is implemented by a three-step iterative refining process, i.e., parameter interval guess, verification, and judgment. In the parameter interval guess step, the parameter interval is divided into several sub-intervals and the true Hamiltonian parameter is guessed in one of them. In the parameter interval verification step, cross verification is applied to verify the accuracy of the guess. In the parameter interval judgment step, an adaptive interval judgment (AIJ) algorithm is designed to determine the sub-interval containing the true Hamiltonian parameter. Numerical results on two typical quantum systems, i.e., two-level quantum systems and three-level quantum systems, demonstrate the effectiveness and superior performance of the proposed approach for quantum Hamiltonian identification.

In this article, the human-in-the-loop leader–follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs’ team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

The identification of risk factors associated with lymph node metastasis (LNM) in gastric cancer will establish a crucial foundation for the implementation of endoscopic operation and multidisciplinary treatment programs.

In this brief, by considering the sensors subject to deception attacks, the observer-based adaptive resilient control problem of a class of nonlinear cyber-physical systems is addressed. Because authentic system state information can be injected with counterfeit data, the impacts of unknown nonlinearity related to unknown attack weights become difficult to be compensated. To circumvent these obstacles, we construct a novel state observer using the system output after being attacked only, and the effects of unknown control gains due to deception attacks on the system are handled by introducing Nussbaum technique. A resilient output feedback control scheme is designed by utilizing the compromised state variables, guaranteeing boundedness for all signals within the closed-loop system. In the end, the efficacy of the proposed control scheme is affirmed through simulation results involving a single-link robot.

This work researches the issue of adaptive fault-tolerant fuzzy tracking control for a class of nonlinear systems in strict-feedback form with quantized inputs. The fuzzy logic systems are utilized to approximate unknown functions, and a fuzzy state observer is built to estimate the unavailable states. Meanwhile, an improved hysteresis quantizer is introduced to achieve the quantized inputs for saving communication resources. To improve the approximation capacities of fuzzy logic systems, the compensated tracking errors and the prediction errors are used to construct the adaptive laws parameters. Furthermore, a composite adaptive fault-tolerant fuzzy control strategy is developed, which can guarantee proper operations of the systems when encountering actuator faults, and overcome the issue of “explosion of complexity” in the backstepping approach. It is strictly demonstrated that the system output can follow a desired signal within a small error zone and all signals of the closed-loop system are bounded. Finally, the simulation results are given to confirm the validity of the presented control strategy.

This article investigates the event-based sensor fault interval estimation and PI controller design for aircraft engines. First, an event-based sensor fault estimation observer (FEO) is constructed to estimate the sensor fault subject to amplitude-bounded disturbance and measurement noise. Then, a codesigned scheme of event-triggered parameters and PI fault-tolerant controller is proposed to save communication resources and guarantee the required performance of the considered system. Furthermore, the estimated information of fault interval is derived via ellipsoid bundles based on the designed FEO. Ultimately, the proposed method is verified by hardware-in-the-loop experiment, which reflects its potential application in propulsion system.

Immunoassays serve as powerful diagnostic tools for early disease screening, process monitoring, and precision treatment. However, the current methods are limited by high costs, prolonged processing times (>2 h), and operational complexities that hinder their widespread application in point-of-care testing. Here, we propose a novel centrifugo-pneumatic reciprocating flowing coupled with spatial confinement strategy, termed PRCM, for ultrafast multiplexed immunoassay of pathogens on a centrifugal microfluidic platform. Each chip consists of four replicated units; each unit allows simultaneous detection of three targets, thereby facilitating high-throughput parallel analysis of multiple targets. The PRCM platform enables sequential execution of critical steps such as solution mixing, reaction, and drainage by coordinating inherent parameters, including motor rotation speed, rotation direction, and acceleration/deceleration. By integrating centrifugal-mediated pneumatic reciprocating flow with spatial confinement strategies, we significantly reduce the duration of immune binding from 30 to 5 min, enabling completion of the entire testing process within 20 min. As proof of concept, we conducted a simultaneous comparative test on- and off-the-microfluidics using 12 negative and positive clinical samples. The outcomes yielded 100% accuracy in detecting the presence or absence of the SARS-CoV-2 virus, thus highlighting the potential of our PRCM system for multiplexed point-of-care immunoassays.

Tuning electronic characteristics of metal–ligand bonds based on reaction pathways to achieve efficient catalytic processes has been widely studied and proven to be feasible in homogeneous catalysis, but it is scarcely investigated in heterogeneous catalysis. Herein, we demonstrate the regulation of the electronic configuration of Ir–O bonds in an Ir single-atom catalyst according to the borane activation mechanism. Ir–O bonds in Ir1/Ni(OH)xare found to be more electron-poor than those in Ir1/NiOx. Despite the mild solvent-free conditions and ambient temperature, Ir1/Ni(OH)xexhibits outstanding performance for the hydroboration of alkenes, furnishing the desired alkylboronic esters with a turnover frequency value of =3060 h–1and 99% anti-Markovnikov selectivity, which is significantly better than that of Ir1/NiOx(42 h–1). It is further proven that the more electron-poor Ir–O bonds as active centers are more oxidative and so benefit the activation of the H–B bond in the reductive pinacolborane.

Graphical Abstract:

This brief concentrates on adaptive 2-bit-triggered tracking control for unknown nonlinear systems with actuator dead-zone. By introducing the low-complexity technique, a structurally simple and computationally efficient controller is constructed to handle the issues of actuator dead-zone and complexity explosion. Meanwhile, a 2-bit-triggered mechanism is installed in the controller part to improve the communication efficiency. Different from the previous event-triggered strategy, the proposed 2-bit-triggered strategy only needs to transmit 2-bit digital numbers for the triggered updates of the control input. The counter-evidence approach proves that the system output is capable of tracking a desired trajectory accurately with tracking errors being limited within prescribed performance functions. Finally, the effectiveness of the proposed control scheme is verified by the simulation results.

This article explores a novel singularity-free command-filtered adaptive fuzzy finite-time tracking control algorithm for the flexible robotic manipulator with dead-zone input. First, by considering the influence of the residual term in two cases, a novel practical finite-time stability criterion is presented, and the settling time is more accurately characterized, which is applied to the design of the flexible robotic manipulator. The unknown nonlinear function in the robotic manipulator is handled by employing an intelligent estimation technique based on fuzzy logic systems. Unlike the related finite-time work, a new adaptive command-filtered finite-time controller is constructed by fusing a piecewise continuous function in each step of the backstepping process, such that the singularity of the control signal of the finite-time results is completely circumvented. Further, the mathematical model of the dead zone is delicately reconstructed such that the dead-zone input can be rigorously designed using effective mathematical techniques. Finally, the robotic manipulator system demonstrates the validity of the designed finite-time algorithm.

Security issues in cyber–physical systems have attracted increasing attention in recent years. In this article, a security problem in a remote estimation application is considered, where an attacker tries to degrade estimation performance via malicious attacks. In our scenario, a smart sensor transmits its innovation to a remote estimator with a residue-based false data detector. Instead of assuming that the attacker launches a consecutive man-in-the-middle attack, we consider the case with intermittent attacks. Reverse attack is a simple attack strategy, which enables an attacker to change signs of the innovation sequences. Therefore, owing to the abovementioned reasons, an event-triggered reverse attack strategy is proposed to degrade the system performance. First, the stealthiness of the event-triggered linear deception attack strategy is studied. Then, the evolution of the estimation error covariance is computed and the reverse attack is proven to be the optimal linear deception attack. We demonstrate that our event-triggered reverse attack is more destructive than a random reverse attack. Furthermore, a convex optimization problem is established to design event-triggered parameters. Comparisons of attack strategies are provided in a numerical example to validate the superiority of the reverse attack.

With the recent development of the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, deep learning methods have achieved promising performance owing to their locally sematic feature extracting ability. Nonetheless, the limited receptive field restricted the convolutional neural networks (CNNs) to represent global contextual and sequential attributes, while visual image transformers (VITs) lose local semantic information. Focusing on these issues, we propose a fractional Fourier image transformer (FrIT) as a backbone network to extract both global and local contexts effectively. In the proposed FrIT framework, HSI and LiDAR data are first fused at the pixel level, and both multisource feature and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contextual and sequential feature extraction. Unlike the attention-based representations in classic VIT, FrIT is capable of speeding up the transformer architectures massively and learning valuable contextual information effectively and efficiently. More significantly, to reduce redundancy and loss of information from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. Five HSI and LiDAR scenes including one newly labeled benchmark are utilized for extensive experiments, showing improvement over both CNNs and VITs.

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor–critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme.

Purpose: This paper aims to study the issues of output reachable set estimation for the linear singular Markovian jump systems (SMJSs) with time-varying delay based on a proportional plus derivative (PD) bumpless transfer (BT) output feedback (OF) control scheme. Design/methodology/approach: To begin with, a sufficient criterion is given in the form of a linear matrix inequality based on the Lyapunov stability theory. Then, a PD-BT OF controller is designed to keep all the output signs of the system are maintain within a predetermined ellipsoid. Finally, numerical and practical examples are used to demonstrate the efficiency of the approach. Findings: Based on PD control and BT control method, an OF control strategy for the linear SMJSs with time-varying delay is proposed. Originality/value: The output reachable set synthesis of linear SMJSs with time-varying delay can be solved by using the proposed approach. Besides, to obtain more general results, the restrictive assumptions of some parameters are removed. Furthermore, a sufficiently small ellipsoid can be obtained by the design scheme adopted in this paper, which reduces the conservatism of the existing results.

For the existing tracking control schemes of flexible-joint robots, precise sensor measurement is an implicit premise. However, idealized sensors are difficult to achieve due to manufacturing technology or other external factors. To this end, this paper further investigates the tracking control problem for flexible-joint robots with unknown measurement sensitivity. Specifically, for such multi-input multi-output Euler-Lagrange systems with completely unknown system dynamics, a novel measurement values-based adaptive control method is proposed by fusing sensitivity information and system variables into Lyapunov function candidates, where the restriction on system states in other the approximation lemma-based results is removed, since unknown nonlinearities are scaled by the structural characteristics of system variables. Above all, even if there are measurement errors, satisfactory tracking performance can be obtained by adjusting the design parameters, which is proved by rigorous theoretical analysis. Finally, hardware experiments further verify the effectiveness of the proposed method. Note to Practitioners—This work is motivated by the trajectory tracking control problem for flexible-joint robots under imprecise sensor measurements. Due to manufacturing technology limitations and component aging, there is inevitably a deviation between the measured values of sensors and real values, and this problem may become more prominent as the working environment of flexible-joint robots tends to become more complex. To our knowledge, most of the existing solutions for flexible-joint robots are developed based on precise sensor measurements, and they may fail to achieve satisfactory performance when real state information is not available. Moreover, the prior knowledge about model parameters and measurement sensitivity is difficult or impossible to exactly obtain in practice, which seriously hinders the further application of control methods that are dependent on system dynamics. To this end, this paper proposes a novel tracking control scheme based on measurement information for flexible-joint robots with unknown measurement sensitivity, where the dependence on model information is eliminated with the elaborately constructed Lyapunov function candidates, and the real tracking error is still adjusted to an acceptable range even if there are measurement errors. Preliminary experiments on a flexible-joint robot developed by Quanser company demonstrate the feasibility and effectiveness of the proposed method. In future studies, designing an effective scheme to achieve direct preset tracking control is the focus of the work.

This paper investigates the deferred output constraint (DOC) issues of high-order random nonlinear systems. The DOC indicates that the system output is constrained within a given range after the system running for a period of time, rather than being constrained at the beginning. This leads to an obstacle that the well used barrier Lyapunov function method is invalid because it is not defined at the initial stage. For this reason, a prescribed-time scaling function and a barrier function are introduced. Then, to more accurately describe the disturbances in nature, this paper considers a more common stochastic process than white noise, which is called colored noise. In addition, a dynamic-memory event-triggered mechanism (DMETM) that takes into account the influence of historical information of internal dynamic variables on trigger conditions is proposed to reduce the waste of communication resources. With the assistance of Lyapunov stability theory and backstepping approach, it is shown that our developed DMETM-based control scheme can guarantee that all signals are bounded in the presence of colored noises. Ultimately, two simulation examples are included to demonstrate the effectiveness of the proposed theory. Note to Practitioners—Since higher-order nonlinear systems can describe nonlinear features in system dynamics, some complex actual systems such as induction motorcycles and space vehicles can be modeled as such systems. In engineering applications, controlled systems are usually affected by random noises from external environment, thus colored noises are introduced in this paper. Compared with white noises, colored noises can describe disturbances in nature more accurately and reasonably. In addition, to ensure that practical systems such as car operation systems and robotic arm systems can work safely, their DOC issues need to be considered. However, the traditional barrier Lyapunov functions (BLFs) cannot solve the DOC problems because they are not defined in the initial stage, hence the prescribed-time scaling and barrier functions are proposed. Meanwhile, to circumvent the problem of limited communication bandwidth, a DMETM with delay terms of internal variables is devised.

This paper investigates the attack-compensated output control problem for a class of Markov jump cyber-physical systems (MJCPSs) subject to mismatched modes. A sub-domain-awareness adaptive probabilistic event-triggered mechanism (APETM) is innovatively developed to fully enhance the control performance of the networked control system. Here, sub-domain-awareness means the network’s communication ability, which may be affected by time delay and network resource. To defend against the cyber-attacks imposed on the communication channel, a predictor-based compensator is constructed to mitigate the impact of attacks on the control performance. Different from the existing works, the mismatch degree between the controller and the system, governed by a random variable obeying any discrete-time distribution over [0, 1], is changing dynamically. A mismatched output feedback controller is designed based on the APETM and attack-compensator, and an augmented closed-loop system is obtained. Both time delay- and triggering threshold- dependent Lyapunov functionals are introduced to perform the asymptotical stability analysis. With a separation technique, a solving algorithm is provided which converts the non-convex analysis conditions into traceable ones. Finally, simulations are conducted to validate the proposed results through a mass-spring-damping system model. Note to Practitioners—Cyber-physical systems (CPSs) have found many applications in the fields such as smart grid, gas distribution systems, and automated manufacturing systems. The open communication network deployed in CPSs face two main challenges including the security of data transmission and the efficiency of resource utilization. Owing to the practical environment and other factors, system parameters may change abruptly, which can be well defined by a Markov model. In the resultant Markov jump system, it is typically assumed that the mode mismatch degree between the controller and the system remains the same, which is unrealistic in a time-varying network environment. Motivated by these observations, this paper investigates the secure output control of Markov jump CPSs (MJCPSs) subject to mismatched modes and unknown cyber-attacks. An adaptive probabilistic event-triggered mechanism is innovatively proposed, which compared with the existing mechanisms strikes a higher level balance between the communication burden and the control performance. And attack-compensated mismatched output controllers are developed to stabilize MJCPSs and meanwhile preserve the security level. Finally, a mass-spring-damping system is adopted to verify the application effectiveness of the proposed results. Note that communication traffic control and secure control are finely involved based on CPSs, making our proposed results more applicable to practical systems.

This paper investigates the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. Firstly, the prescribed-time control is realized based on scale function and descending power coordinate transformation. Then, effective nonlinear terms are designed in the controllers to compensate for state pollution caused by sensor faults. Furthermore, the Nussbaum gain function is introduced and it can be proven that the derivative of its variable is bounded, which is of significance to analyze the invariant set. The proposed control scheme guarantees that system states converge to zero within the specified time in advance and all signals of the closed-loop system are bounded. Finally, a numerical simulation and a practical simulation based on the Nomoto ship model intuitively show the feasibility of the scheme. Note to Practitioners—This study is motivated by achieving the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. With the improvement of automation, the prescribed-time control that the convergence time can be arbitrarily specified in advance has been applied to many time-critical fields, such as missile guidance, emergency braking, and so on. It is noted that hypersonic weapons, uncalibrated visual servo control, and other practical models have the characteristic of unknown control direction, and most of the existing prescribed-time control schemes are not suitable for these practical systems. In addition, since the hovercraft power systems and aircraft systems are in a harsh environment for a long time, it can easily lead to sensor faults resulting in major accidents. In this study, based on the descending power coordinate transformation, the prescribed-time controllers with unique nonlinear terms are designed to deal with the above challenges.

In this paper, for a class of uncertain nonlinear systems, a low-computation design scheme for fault detection and performance recovery based on deferred replacement actuators is proposed. Different from the existing two mainstream adaptive fault-tolerant control schemes, the proposed method does not require prior knowledge of fault models, nor does it require multiple actuators working in parallel simultaneously to mitigate the impact of faults. The disadvantage of the former is that most of the considered fault models cannot cover all fault types, while the latter adds unnecessary wear and tear to actuators that do not generate faults. In order to overcome the shortcomings of the existing fault-tolerant control, a method with deferred actuator replacement is proposed. By designing a fault detection function and a shifting function, new error variables are reconstructed to achieve performance recovery. In addition, a computationally efficient design scheme is established through the idea of constraint control. Unlike the existing advanced technology, firstly, dealing with the problem of complexity explosion from a new perspective and ensuring the rationality of assumptions; secondly, analyze the controllability of system states during the deferred replacement stage. Finally, simulation results on a twin otter aircraft system verify the effectiveness of the proposed scheme. Note to Practitioners—For widely used modern control systems such as aircraft systems, self-driving systems, etc., it is extremely difficult to diagnose and repair the sudden fault behavior in a short time, and in this process, the adverse impact caused by the serious decline of the system performance is huge. Inspired by the practical application of airbus states in the flight control system, a fault-tolerant control scheme of deferred replacement actuators is designed in this paper. In addition, by designing a low-computation control scheme, the assumptions that are difficult to achieve in practice are relaxed, and the structure of the controller is very simple without the help of any auxiliary design. After fault detection, the replacement actuators strategy successfully realizes the recovery of the system performance within the specified time.

In this paper, a novel hierarchical sliding-mode surface (HSMS)-based adaptive event-triggered optimal control (ETOC) method is proposed for switched nonlinear systems using single-critic adaptive dynamic programming (ADP). Initially, an ameliorative HSMS containing all system states is constructed to improve the system response rate. With positive quadratic functions being assigned for the developed HSMS-based cost function, the original stabilization problem is further transformed into an array of ETOC problems. Subsequently, by implementing a single-critic neural network (NN) whose weights are tuned based on an aperiodic manner and the historical data, a series of event-triggered Hamilton-Jacobi-Bellman (HJB) equations are approximately solved. Meanwhile, the condition for the persistence of excitation existing in the traditional actor-critic dual NNs framework is relaxed. Furthermore, the uniformly ultimate boundedness (UUB) of all signals in the closed-loop system is guaranteed by the Lyapunov extension theorem. Finally, a practical continuous stirred tank reactor plant is provided to substantiate the efficacy of the proposed HSMS-based adaptive single-critic ETOC method. Note to Practitioners—Switched nonlinear systems have attracted much attention due to their flexibility in characterizing multi-mode features in practice. The control synthesis and stability analysis of switched nonlinear systems have been the research focus in the control field. However, with the development of adaptive dynamic programming (ADP), it is no longer limited to the study of control synthesis and stability of switched systems. How to improve the response rate and achieve optimal control of switched nonlinear systems with less control cost becomes critical. On the other hand, the persistent information transmission of control signals not only occupies more bandwidth, but also causes severe transmission delays, thus it is important to conduct the signal transmission only when necessary. This paper discusses the optimal control problem for switched nonlinear systems. Meanwhile, an event-triggered adaptive dynamic programming optimal control policy based on a hierarchical sliding-mode surface is proposed, which consumes less control cost while improving the system response rate and saving communication resources.

This paper investigates the event-triggered-based adaptive bipartite finite-time tracking control problem of nonlinear nonstrict-feedback coopetition multi-agent systems (MASs) with the time-varying disturbances. First, the major design difficulties generated by the entirely unknown nonlinear functions containing all states are solved by utilizing the approximation property of radial basis function neural networks (RBF NNs) and the structural feature of Gaussian functions. Then in the backstepping procedure, the issue of “explosion of complexity” is handled by combining the adaptive neural approach and the command filter technique, which simplified the complexity of the controllers for all the agents. Meanwhile, the novel compensation signals are designed, which skillfully eliminate the error influence caused by the filters. Moreover, to save the communication resources, the relative threshold event-triggered control (ETC) scheme is presented for the designed controllers, and there is no Zeno phenomenon. Overall, it is shown that the new proposed control approach drives the tracking errors to the desired neighborhood of the origin in an almost fast finite time, and all the signals in the closed-loop systems are almost fast finite-time bounded. Finally, the numerical and practical simulation results are both given to show the validity of the obtained design method. Note to Practitioners—This paper presents the event-triggered adaptive bipartite finite-time tracking control scheme for the nonlinear nonstrict-feedback coopetition MASs with the time-varying disturbances, which can be used to model most practical systems such as sensor network, submarine underwater robot and space satellites. Although the bipartite control problem has been studied a lot, the bipartite finite-time tracking control problem for nonlinear nonstrict-feedback coopetition MASs with the time-varying disturbances is still open and challenging. The proposed approach guarantees that the tracking errors can converge to the desired neighborhood of the origin in an almost fast finite time. In addition, the ETC strategy is proposed to address the limited power consumption. A remarkable point for this paper is that the obtained results are demonstrated by the simulation examples, which shows the validity of the proposed approach.

This paper proposes an adaptive bipartite secure consensus asymptotic tracking control scheme based on event-triggered strategy for the nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. First, by incorporating the concept of shortest path into the hierarchical algorithm, the bipartite consensus control problem in the presence of unbalanced communication topology is successfully addressed. Further, an anti-attack bipartite control strategy by designing the improved forms of the tracking errors and virtual controllers is proposed under DoS attacks. Then, a modified event-triggered mechanism based on the relative threshold strategy is proposed, which can ensure that the bipartite consensus tracking errors converge to zero asymptotically by utilizing the Barbalat’s Lemma. Under the Lyapunov stability theory, it is guaranteed that the outputs of all agents can reach agreement with an identical magnitude but opposite signs and all the signals of the closed-loop MASs are bounded. Finally, the simulation results on the model of damping pendulums are given to verify the efficiency of the proposed secure controller. Note to Practitioners—In this paper, the adaptive bipartite secure consensus asymptotic tracking control problem is considered for nonlinear MASs subject to DoS attacks, whose models can describe many critical applications, such as bidirectional formation of unmanned vehicles and robots. The research on the secure tracking control problem will be rather complicated yet challenging if the DoS attacks of the communication channels are taken into account in the insecure complex network environment. In addition, the event-triggered mechanism is used to reduce the usage of communication resources, which makes the proposed control scheme more easier to implement and more friendly for control engineers.

Mitochondrial homeostasis is coordinated through communication between mitochondria and the nucleus. In response to stress, mitochondria generate retrograde signals to protect against their dysfunction by activating the expression of nuclear genes involved in metabolic reprogramming. However, the mediators associated with mitochondria-to-nucleus communication pathways remain to be clarified. Here, we identified that hnRNPH1 functions as a pivotal mediator of mitochondrial retrograde signaling to maintain mitochondrial homeostasis. hnRNPH1 accumulates in the nucleus following mitochondrial stress in a 5′-adenosine monophosphate-activated protein kinase (AMPK)-dependent manner. Accordingly, hnRNPH1 interacts with the transcription factor NRF1 and binds to the DRP1 promoter, enhancing the transcription of DRP1. Furthermore, in the cytoplasm, hnRNPH1 directly interacts with DRP1 and enhances DRP1 Ser616 phosphorylation, thereby increasing DRP1 translocation to mitochondrial outer membranes and triggering mitochondrial fission. Collectively, our findings reveal a novel role for hnRNPH1 in the mitochondrial-nuclear communication pathway to maintain mitochondrial homeostasis under stress and suggest that it may be a potential target for mitochondrial dysfunction diseases.

In the field of remote sensing (RS) data processing, light detection and ranging (LiDAR)-derived digital surface model (DSM), which is capable of effectively reflecting elevation information, has significant value in earth observation applications. In this work, we proposed a multiscale neighborhood information fusion (MNIF) network for classification of RS LiDAR images. Specifically, they capture the details and overall characteristics of the data through multiscale patches simultaneously, with the information from different scales complementing one another. Subsequently, a spatial-aware region (SR) attention module is utilized to emphasize distinctive features specific to each category. Furthermore, since the input feature maps from different scales encompass distinct neighborhood information, the cross-scale convolutional kernels are incorporated into the designed optimized feature extractor (OFE). Experimental results based on two DSM datasets named Houston and Trento in the 2013 GRSS Data Fusion Competition demonstrate that the proposed MNIF network can achieve superior classification performance compared with several existing methods.

In this article, an adaptive fuzzy bipartite formation tracking control strategy of nonlinear multiagent systems (MASs) is developed under a directed communication topology. Fuzzy logic systems are utilized to approximate the unknown nonlinear dynamics. Then, considering MASs with both cooperative and competitive relationships, the proposed bipartite formation tracking strategy allows each agent to operate according to their specific advantages. By employing a nonlinear state transformation function, the original constrained outputs of the MASs are converted into unconstrained ones. Meanwhile, a prescribed-time shifting function is introduced to handle the deferred output constraints, by which the singularity problem generated from the denominator of nonlinear state transformation functions is avoided. In addition, considering that the communication bandwidth is limited, a distributed dynamic self-triggered control (DSTC) mechanism is constructed to improve the transmission efficiency. Different from the traditional self-triggered control strategy, the proposed DSTC strategy allows triggered intervals to be adjusted dynamically according to bipartite formation tracking errors, which enables the DSTC strategy to compromise communication burden and system performances dynamically. Finally, two simulation examples are given to illustrate the validity of the proposed control scheme.

This article focuses on the adaptive fuzzy practical predefined-time bipartite consensus tracking control (BCTC) problem for heterogeneous nonlinear multiagent systems (HNMASs) with actuator faults. First, the fuzzy logic systems are used to approximate the unknown nonlinear functions. Then, the partial loss of effectiveness and bias fault of actuator are considered simultaneously in the HNMASs, which is effectively handled by using adaptive compensation technology. In addition, the developed bipartite consensus tracking control protocol based on a practical predefined-time strategy not only ensures the fast convergence of the studied systems, but also predetermines the convergence time not relied on the initial conditions. The theoretical result shows that all signals of the closed-loop system are semiglobally uniformly predefined-time bounded, and the BCTC performance is guaranteed within the predefined time. Finally, the simulation example based on the agents of different orders shows the validity of the obtained results.

This article is focused on the problem of periodic event-triggered control (PETC) and dynamic periodic event-triggered control (DPETC) for nonlinear cyber-physical systems (CPSs) under denial-of-service (DoS) attacks and exogenous disturbances. The analysis relies on a new model to design PETC and DPETC schemes such that the nonlinear CPSs are resilient to a class of malicious attacks which can be characterized by attacking frequency and attacking duration. By developing average dwell-time automaton and a time-ratio monitor to describe frequency and duration of the DoS attacks, general hybrid dynamical frameworks are first proposed for event-triggered nonlinear CPSs. Based on the established models, novel Lyapunov functions are constructed. Furthermore, thanks to the Lyapunov-based approach, $L_{2}$-gain performance of the considered nonlinear CPSs under DoS attacks is analyzed. Finally, the feasibility of the theoretical findings is demonstrated by a numerical example.

The security problem for a kind of switched nonlinear systems with denial-of-service (DoS) attacks is addressed here with a novel event-triggered neural network (NN) adaptive control technique. The provided event-triggered control algorithm, compared to the current output feedback control schemes on continuous-time systems, not only conserves communication resources but also defends against DoS attacks throughout the system stabilization process. First, a novel adaptive NN switching observer is proposed by examining the stability of each subsystem in the sleep and active intervals of the attack, which makes the standard backstepping controller workable even in the DoS attacks scenario. Further, an event-triggered secure control strategy is established by creating a common coordinate transformation, which ensures the bound of all the signals in the resultant system under arbitrary switching and prevents the occurrence of Zeno behavior. A first-order filter is developed to fix the complexity explosion problem in backstepping design. Finally, a continuous stirred tank reactor example is presented to support the established method.

A distributed adaptive secure control algorithm is proposed for asynchronous switching nonlinear multi-agent systems (MASs) in pure-feedback form while considering the sensor attacks and actuator faults, which eliminates the effect of attack signals, actuator faults and uncertainties without knowing the sign of the control gain functions. Since the system states measured by the sensors are corrupted by the malicious attacker, only the post-attack system states can be obtained. Thus, an improved coordinate transformation is constructed by using the post-attack system states and a backstepping method based on the improved coordinate transformation is developed to solve the consensus tracking control problem of the researched systems under sensor attacks. The common Lyapunov function is designed to ensure the stability of all asynchronous switching subsystems of each agent. In the end, a simulation example is presented to illustrate the theoretical results’ validity. Note to Practitioners—This paper deal with the distributed adaptive secure consensus tracking control problem for asynchronous switching nonlinear MASs, whose models can describe many phenomena in nature such as biological processes, robot team formation and sensor networks. It is extremely challenging to achieve the tracking control problem of the researched asynchronous switching nonlinear system in an unsafe network environment, where the unknown sensor attacks signals, actuator faults coefficients, and randomly switched non-affine nonlinearities exist together for the followers. Furthermore, the control design and stability analysis of the researched system is based on the common Lyapunov approach, which makes the developed approach more engineering oriented.

In this article, the asymptotic tracking control problem for a class of nonlinear multi-agent systems (MASs) is researched by the combination of radial basis function neural networks (RBF NNs) and an improved dynamic surface control (DSC) technology. It’s important to emphasize that the MASs studied in this article are nonlinear and nonstrict-feedback systems, where the nonlinear functions are unknown. In order to satisfy the requirement that all items in the controller must be available, the unknown nonlinearities in the system are flexibly approximated by utilizing RBF NNs technique. Moreover, the issue of “complexity explosion” in the backstepping procedure is handled by improving the traditional DSC technology, and meanwhile, the influences of the boundary layers caused by the filters in the DSC procedure are eliminated skillfully through the compensation terms. In addition, the relative threshold event-triggered strategy is developed for the designed controllers to reduce the waste of communication resources, where Zeno phenomenon is successfully avoided. It is observed that the new presented control strategy ensures that all the closed-loop systems variables are uniformly ultimately bounded (UUB), and furthermore all the outputs of followers are able to track the output of the leader with zero tracking errors. Finally, the simulation results are presented to show the effectiveness of the obtained design scheme.

This article proposes an adaptive fixed-time output feedback tracking control scheme for a class of nonstrict-feedback stochastic nonlinear systems subjected to unmodeled dynamics and dynamic disturbances. To address the uncertainty arising from nonlinear terms, fuzzy logic systems are used as a universal approximator. Moreover, a state observer is utilized to estimate the unmeasured states. Then, the dynamic signal is introduced into the design process of the controller to solve unmodeled dynamics. Through stability analysis, the proposed new output feedback tracking controller guarantees the boundedness of all the signals, and the tracking error converges to a small residual set within a fixed time. The results of the simulation illustrate the effectiveness of the developed controller.

This article considers the regional consensus problem for nonlinear multiagent systems subject to actuator saturation, and the nonlinear function is supposed to satisfy incremental quadratic constraints, which is a more completed characterization of nonlinearity. Compared with the traditional linear feedback control, a novel saturated nonlinear control protocol is generalized under the framework of composite Laplacian quadratics function. The introduction of the novel function makes the estimation on the domain of consensus larger than the existing Laplacian quadratic function-based methods. The sufficient conditions for the consensus of multiagent systems are derived by the forms of bilinear matrix inequalities, which cover the existing linear matrix inequality-based conditions as special cases. Finally, a numerical simulation case pertaining to aircraft illustrates the validity as well as the superiority of the proposed approach.

In this article, an event-based physical watermark is proposed and analyzed. It is well known that adding physical watermarks into cyber-physical systems (CPSs) is an effective way to combat malicious attacks. However, the classical ways to adding physical watermarks are based on time, which leads to great decreases in linear–quadratic Gaussian (LQG) performances while increasing detection rates. To deal with the performance loss after adding physical watermarks, an event-based physical watermark is designed to detect replay attacks. A stochastic event scheduler based on the innovation sequence is constructed to indicate adding physical watermarks or not. It is shown that the probability of adding physical watermarks will rise if the system is under attacks. Based on the frequency of adding watermarks, a count detector is proposed, which provides lower memory and computational cost. LQG performance and detection rates of a $\chi ^{2}$ detector are further characterized. Herein, it is formally proved that the event-based independent identically distributed (i.i.d.) watermark is more efficient than the time-based i.i.d. watermark. Furthermore, we investigate the optimal parameters to enlarge detection performance in certain conditions. Finally, some simulations are given to show the superiority of our proposed event-based physical watermark.

This article presents a co-design method of dynamic periodic event-triggering mechanism (DPETM) and output-based control law for networked control systems (NCSs) subject to inevitable imperfections such as limited network resources, exogenous disturbances and successive packet dropouts. The developed DPETM has the potential to make full use of scarce network resources compared to the periodic sampled-data control on the one hand, and is natural for practical digital platforms instead of a discretized version of continuous event-triggered control (CETC) one the other hand. In this paper, the whole closed-loop system is modeled a unified hybrid system by incorporating bilateral DPETM, successive packet dropouts effects and external disturbances, and an improved hybrid Lyapunov function is constructed for stability analysis. Based on the hybrid framework, some sufficient conditions are presented to guarantee $\mathcal {L}_{2}$ -stability of the system while tolerating a maximum allowable number of successive packet dropouts (MANSD). Furthermore, new linear matrix inequality (LMI) conditions for co-design of control law and triggering strategy are derived by introducing some constraints with the free variables and a modified Young’s inequality. In addition, this paper considers two scenarios relating to whether an acknowledgment scheme is employed or not. Finally, the effectiveness of the proposed method is verified by two illustrated examples and comparative studies. Note to Practitioners—With the increasing requirement for reducing unnecessary waste of communication resources in modern industries, it is necessary to consider an alternative paradigm of the time-triggered control. In many digital control applications such as smart power grid, vehicle and remote surgery, the control tasks are often executed periodically, which makes it difficult for the theoretical results of CETC to be directly applied to practical digital platforms. The DPETM proposed in this paper, which only verifies the triggering conditions periodically at each sampling instant, is more suitable for practical digital platforms such as time-sliced embedded architectures compared to CETC. This paper investigates the stability problem for NCSs with DPETM subject to packet dropouts. Different from most of the existing literature on randomness of packet loss, this paper focuses on impact of the continuity of packet dropouts on system performance. Based on the hybrid framework and the Lyapunov approach, a co- design methodology of triggering condition and control law is proposed to ensure that the system satisfies the desired performance and can admit a MANSD.

This article is concerned with the mismatched quantized $H_{\infty }$ output-feedback control of fuzzy Markov jump systems via a dynamic guaranteed cost triggering scheme. An event generator and a quantizer are set up at the sensor-to-controller side and the controller-to-actuator side, respectively. The quantization scheme is presented in terms of a multichannel configuration with different decoder/encoder parameters. A guaranteed cost dynamic event-triggered mechanism is built on instantaneous and averaged triggering errors, output cost, and preset bounds. A composite controller consisting of a static output-feedback and a nonlinear compensation is constructed to meet the desired system performance. Based on the Lyapunov stability theory, sufficient conditions are obtained such that the closed-loop system is stochastically stable with the prescribed $H_{\infty }$ performance. A structural vertex separation technique and Finsler's Lemma are employed to decouple the control gain, the quantizer parameters, and the Lyapunov variable. Finally, the validity of proposed scheme is verified by a circuit example.

This article concerns innovative adaptive sliding mode control governed by an observer for a time-varying fuzzy system constrained by fractional Brownian motion with $H \in (\frac{1}{2},1)$ under Markovian jump with uncertain transition rates. First, for an uncertain signal, a Takagi-Sugeno (T-S) observer is constructed. Furthermore, in accordance with the unique feature of the coefficient matrix for an uncertain block, the observation system is divided into two low-order subsystems through regular transformation. Subsequently, a common and fixed sliding surface function that does not change with jump is constructed; this is one of the characteristics that differ from the characteristics of the integral switching surface designed by many researchers who have studied the Markovian jump. An adaptive controller is also designed and finite-time reachability analysis is performed by using it. Additionally, a stochastic stability study is performed by constructing Lyapunov--Krasovskii functional, introducing slack matrices and utilizing linear matrix inequalities. Finally, a numerical simulation was conducted to verify the reliability of the proposed approach.

This article focuses on the problem of resilient event-triggered $H_{\infty }$ filtering for networked switched Takagi–Sugeno (T-S) fuzzy systems in the presence of Denial-of-Service (DoS) attacks. To improve the utilization efficiency of communication resources and resist the influence of attacks, the resilient mode-dependent event-triggered strategy (ETS) is proposed to determine the necessary data transmission. Different from the existing ETSs for the switching linear systems, the proposed strategy not only removes the two adjacent trigger instants that are strictly less than the average dwell time constraint but also releases data immediately at the end of the attack to mitigate its side effects. Based on the transmitted switching signal and measurable output, the switching fuzzy filter is constructed to estimate the target signal. Unlike the traditional switching filter design, the transmission signals are affected by intermittent interruption of DoS attacks and ETS leads to the asynchronous behavior of system mode and filter mode, which increases the difficulty of filtering error system analysis and filter design. To overcome this challenge, a novel multiple Lyapunov function related to attack signals, system modes, and filter modes is established to analyze the exponential stability and $H_{\infty }$ performance of the estimation error system. Furthermore, co-design methods regarding filter gains and event-triggered parameters are given. Finally, simulation results are carried out to substantiate the feasibility of the proposed approach.

This article presents an asymptotic tracking controller for high-order uncertain nonlinear systems. Compared to the existing results, some restrictive assumptions are relaxed and the requirement of the variables being bounded is removed. At the same time, a prescribed time tracking control strategy is constructed to make that the system has better-transient performance during operation. Specifically, a global prescribed time function is proposed and an unconstrained variable is constructed to replace the constrained tracking error. Under this framework, an appealing feature of the designed controller is that the tracking error can converge to a specified range within a prescribed time and eventually converge to zero asymptotically starting from any initial conditions, where the prescribed time and accuracy can be directly given. Finally, the simulation results validate the effectiveness of the control strategy.

Based on energy-based multiple Lyapunov functions approaches, this article concerns with stabilization and $\mathcal {H}_{\infty }$ control for switched uncertain port-controlled Hamiltonian (SUPCH) systems with mode-dependent average dwell time (MDADT) switching. To better deal with the uncertainties of each mode, an improved MDADT scheme along with corresponding constraint conditions is established, and the criterion on globally uniformly asymptotical stability is presented for SUPCH systems in unforced form under the new MDADT mechanism. Subsequently, based on mode-dependent state feedback (MDSF) strategies, the sufficient conditions are derived for stabilization of SUPCH systems. Furthermore, to solve $\mathcal {H}_{\infty }$ control problem for SUPCH systems with disturbances, a set of MDSF controllers are constructed, and the $\mathcal {H}_{\infty }$ control criterion is obtained by the improved MDADT scheme. Finally, as an application, some simulations on a switched nonlinear RLC circuit system with structural uncertainties are carried out by the proposed control strategies.

In this brief, the problem of event-triggered reachable set (RS) control for Takagi-Sugeno fuzzy singular perturbed systems (FSPSs) with continuous time-varying delays is studied. Originally, Lyapunov stability theory and linear matrix inequalities are applied to limit the systems state by the RS ellipsoid. For the designed event-triggered state feedback controller, the free-weighting matrix method and the matrix decoupling technology are used to obtain the controller gains, and then the upper bound of the stability of the FSPSs is obtained. Finally, A circuit example is provided to demonstrate the efficacy of the findings.