Search

Berberine (BBR), a widely recognized traditional Chinese medicine, has attracted considerable attention for its promising anti‐inflammatory effects. The activation of nuclear factor erythroid 2‐related factor 2 (Nrf2) effectively safeguards against organ damage stemming from sepsis‐induced oxidative stress and inflammatory responses. This study examined the potential of BBR in alleviating sepsis‐induced acute gastric injury, with a particular focus on elucidating whether its mechanism of action involves the activation of the Nrf2 signaling pathway. Following intraperitoneal injection of BBR, mice were subjected to the cecal ligation and puncture (CLP) method to induce sepsis. In vitro experiments involved pre‐treating the normal gastric epithelial cells (GES‐1) with BBR, followed by treatment with lipopolysaccharide (LPS). Functional assays were then performed to assess cell proliferation and apoptosis. To validate the role of Nrf2 in pyroptosis and inflammation, siRNA targeting Nrf2 (si‐Nrf2) was transfected into LPS‐treated GES‐1 cells. Additionally, mice were administered the Nrf2 inhibitor ML385 to confirm the protective effects of BBR in vivo. BBR displayed a dose‐dependent effect in mitigating gastric tissue damage, suppressing the release of inflammatory cytokines, and reducing the expression of NLRP3, ASC, and GSDMD‐N. In vitro, BBR fostered GES‐1 cell proliferation, hindered apoptosis, and suppressed the levels of TNF‐α, IL‐18, IL‐1β, NLRP3, ASC, and GSDMD‐N. Further analysis revealed that knocking down Nrf2 reversed BBR's inhibitory effect on pyroptosis in LPS‐treated GES‐1 cells. Through binding to Keap1, BBR efficiently prevented the ubiquitination and degradation of Nrf2, ultimately promoting its nuclear translocation. In vivo experiments confirmed that ML385 reversed the protective effect of BBR on pyroptosis and inflammation. Our research reveals that BBR interacts with Keap1 to activate the Keap1/Nrf2 signaling pathway in gastric epithelial cells, thereby suppressing pyroptosis and inflammation in sepsis‐induced acute gastric injury.

In this article, we propose an approximate optimal control strategy for a class of nonlinear multiagent system pursuit–evasion games. Herein, multiple pursuers aim to capture multiple evaders trying to evade capture. Under the competitive framework, agents not only pursue their individual goals but also consider coordination with their teammates to achieve collective objectives. However, maintaining cohesion with teammates in existing distributed control methods has always been a challenge. To enhance team coordination, we employ a graph-theoretic approach to represent the relationships between agents. Based on this, we design a dynamic target graph algorithm to enhance the coordination among pursuers. The approximate optimal strategies for each agent are solved by utilizing the Hamilton–Jacobi–Isaacs equations of the system. As solving these equations becomes computationally intensive in multiagent scenarios, we propose a value-based single network adaptive critic network architecture. In addition, we consider scenarios where the numbers of agents on both sides are inconsistent and address the phenomenon of input saturation. Moreover, we provide sufficient conditions to prove the system's stability. Finally, simulations conducted in two representative scenarios, multiple-pursuer-one-evader and multiple-pursuer-multiple-evader, demonstrate the effectiveness of our proposed algorithm.

In this article, a novel visual-inertial-lidar odometry (VILO) system named TCK-VILO is proposed to assist unmanned ground vehicles in reaching high localization performance. We introduce an adaptive sensor reliability evaluation method to configure weights of visual and lidar measurements dynamically in the backend optimization, which can improve both the accuracy and robustness of the localization in real-world outdoor scenarios. A two-stage initialization method is proposed to initialize the system by solving the spatio-temporal alignment of system states. In the TCK-VILO system, a lightweight frontend is designed to update system states while lidar or visual data is fed into the system, and then a tightly-coupled and keyframe-based backend is used to refine system states. We evaluate the proposed TCK-VILO pipeline on the public Newer College dataset and a self-collected real-world dataset. Experimental results show that our system can achieve low drifts in various challenging scenes and outperforms competing state-of-the-art VILO systems.

In this article, the distributed cognitive learning algorithm of cooperative–competitive multiagent systems (MASs) on undirected graphs is studied, using parabolic equations as a model. Two cases are considered: 1) without disturbance and 2) with boundary disturbance. A bipartite consensus learning strategy is designed for the disturbance-free case, enabling agents to cognitively learn cooperative and adversarial information transmitted by their neighbors, thus, reaching a synergistic consensus with other agents. For the case with boundary disturbance, an auxiliary system is first introduced and a disturbance observer is designed to estimate the disturbance. Then, a cognitive learning algorithm is designed, in which agents constantly adjust their behavior by sensing the external environment to make complex behavioral decisions. Based on the proposed algorithms, the well-posedness of the closed-loop system is verified using semi-group theory, and the bipartite consensus of the MASs is discussed using Lyapunov’s direct method. Finally, the effectiveness of the proposed algorithms is verified using the finite difference method and Chebyshev spectral method.

This paper proposes an adaptive fuzzy output feedback control approach for quadrotor unmanned aerial vehicles (QUAV) with stochastic disturbances, besides which we consider the unmeasurable states and unknown nonlinear functions. The QUAV system contains stochastic terms which are not bounded and not differentiable. By combining the It $\hat {\rm \textbf {o}}$ differential equation with finite-time theory, a novel stochastic finite-time stability controller for QUAV is raised for the first time. The fuzzy logic system (FLS) is utilized to approximate the unknown nonlinear functions in the model. Dynamic surface control technology is introduced to reduce the complexity of differential. Furthermore, an observer with FLS is designed through the adaptive backstepping technique to estimate the immeasurable states. It is proved that this control approach can ensure that all states of the closed-loop QUAV system are semi-global and finite-time stable in probability. Meanwhile, the errors of system outputs and observer converge to a small neighborhood of origin. The simulation results show the effectiveness of the proposed method.Note to Practitioners—In practical applications, many systems are often affected by internal and external disturbances, and not all of them can be described by mathematical models, so they are called stochastic disturbances. Taking the QUAV as an example, when the system is disturbed by internal parameters, external environment, system input, sensor error, and other stochastic factors, the deterministic system can no longer accurately describe the controlled object. In this paper, the QUAV is regarded as a stochastic system for the first time, and a novel controller is designed to solve the above problems. In addition, the QUAV needs fast action in some applications, such as emergency collision avoidance, which has high requirements for the convergence performance of the controller. Therefore, studying the finite-time control for QUAV systems with stochastic disturbances is necessary. At the same time, this paper also considers the problem that the state is not measurable and the system contains unknown nonlinear equations.

This article presents a decentralized adaptive event-triggered fault-tolerant control (FTC) method for switched interconnected nonlinear systems with multiple unpredictable actuator faults, input saturation, and asymmetrical time-varying full-state constraints. Uncertain items and interconnected items are identified by a fuzzy logic system (FLS), and a state observer is designed to estimate the unavailable states. By integrating backstepping and command filtering technology, the shortcomings of computational explosion in traditional backstepping are solved. The barrier Lyapunov function and the auxiliary system are designed to solve the full-state time-varying constraints and input saturation respectively. By adopting an event-triggered mechanism and estimating the boundaries of unknown fault parameters, an event-triggered FTC controller is designed to achieve the ability of flexibly coordinate control performance and communication burden, and compensate for unpredictable actuator failures. The scheme realizes the situation where arbitrary switching signals and states are unavailable, whether a fault occurs or not, all signals are bounded, and the input and state satisfy the constraints. Finally, the effectiveness of the proposed control method is verified by simulation example.

In this article, for a class of uncertain nonlinear multiagent systems (MASs) with external disturbances, it investigates adaptive state-based event-triggered predefined-time fuzzy formation control protocol. In order to decrease the hardware requirements of the systems and the interaction between agents, the protocol adopts the state-based event-triggered mechanism (ETM). Compared with most output-triggered mechanisms, the mechanism can reduce the computation and transmission load more effectively, and requires less information interaction. The fuzzy logic systems and distributed disturbance observer are used respectively to handle uncertain nonlinear functions and external disturbances in MASs. Furthermore, considering the convergence rate of the closed-loop system, a predefined-time tuning function is introduced to preset the system stabilization time. Considering the problem of decreasing the computational complexity, the dynamic surface control technique is introduced. The stability of the controlled system is analyzed by Lyapunov function theory. Finally, the feasibility and effectiveness of the control strategy are validated by the simulation.

This paper investigates the finite-time consensus problem for nonlinear parabolic networks by designing a new tracking controller. For undirected topology, the newly designed controller allows to optimize the consensus time by adjusting the parameter $\beta (0< \beta < 1 )$β(0<β<1). First, the neural network approximation property is utilized to counteract the uncertain nonlinear dynamics of agents, and the event-triggered mechanism is designed to save energy and reduce the communication burden. Second, a tracking control protocol is proposed based on event-triggered mechanism, which drives the multi-agent system to reach leader-follower consensus in finite time. Then, by considering appropriate Lyapunov generalization functions and using some important inequalities, the sufficient condition for achieving finite-time consensus in the multi-agent system is obtained. Finally, the effectiveness of the presented method is verified by simulation.

This article deals with adaptive event-triggering control for nonholonomic systems. Based on state feedback, we fulfill the cooperative design of control law and event-triggering strategy. The crucial method is to use the set-valued map to cover the discontinuous set of event sampling. At the same time, combining the set-valued derivative with backstepping technique to achieve adaptive event control and neural networks are used to fit the unknown functions. The nonholonomic constraints of the system are removed by state-scale systematic design. Through transforming the event-triggering control system into a cascade network with two layers of subsystems, the stability of the entire system is proved based on the input-to-state stable small-gain theorem. The proof that Zeno phenomenon does not occur works in two ways: on the one hand, it ensures that the event trigger is effective; on the other hand, it ensures that there are limited jump discontinuities so that adaptive control can be carried out. Finally, the effectiveness of the adaptive event-triggering control method based on the small-gain theorem is verified by simulation.

An optimal tracking control system using neural adaptive techniques is introduced for nonlinear systems subjected to time delay and inequality constraints, which is partially uncertain. The nonlinear inequality constraints and partial uncertain time delay of the state are considered in the discrete-time nonlinear system. By transforming the inequality constraint information into augmented system state variables, and using the precompensator method, an augmentation system that contains constraints and transformed controller information is obtained. The Lyapunov–Krasovskii functionals (LKFs) can be used to deal with the partial uncertain state time delay. Subsequently, the optimal controller, the long-term cost function, the uncertain resistance, and system dynamics can be approximated by the action, critic, the disturbance, and the state estimation NNs, and suitable adaptive laws are obtained. Furthermore, the uniform ultimate boundedness (UUB) of the signals in the closed-loop control system can be obtained by the designed near-optimal controller. The inequality constraints are satisfied and the challenge arising from partial uncertain time delay has been successfully addressed, while a numerical simulation verification example is presented.

This article addresses a distributed time-varying optimal formation protocol for a class of second-order uncertain nonlinear dynamic multiagent systems (MASs) based on an adaptive neural network (NN) state observer through the backstepping method and simplified reinforcement learning (RL). Each follower agent is subjected to only local information and measurable partial states due to actual sensor limitations. In view of the distributed optimized formation strategic needs, the uncertain nonlinear dynamics and undetectable states may jointly affect the stability of the time-varying cooperative formation control. Furthermore, focusing on Hamilton–Jacobi–Bellman optimization, it is almost incapable of directly dealing with unknown equations. Above uncertainty and immeasurability processed by adaptive state observer and NN simplified RL are further designed to achieve desired second-order formation configuration at the least cost. The optimization protocol can not only solve the undetectable states and realize the prescribed time-varying formation performance on the premise that all the errors are SGUUB, but also prove the stability and update the critics and actors easily. Through the above-mentioned approaches offer an optimal control scheme to address time-varying formation control. Finally, the validity of the theoretical method is proven by the Lyapunov stability theory and digital simulation.

This article studies the predefined time control design issue for uncertain nonlinear systems with full-state error constraints and unmeasurable states for the first time. Compared with existing works, this study enables the controlled system to stabilize within a predetermined time and ensures that the full-state tracking errors converge to a desired control accuracy range, even in the absence of measurable state information. Fuzzy logic systems (FLSs) are applied to handle unknown nonlinear dynamics, and FLSs-based nonlinear state observer is constructed to estimate unmeasurable states. With the universal barrier Lyapunov function and the dynamic surface control technique, an output-feedback-based full-state error constrained control strategy with predefined time stability is proposed, in which a nonsingular predefined time filter is constructed to avoid high computational complexity. Under this control strategy, the predefined time stability of the closed-loop system is achieved and the full-state error constraints are satisfied. Finally, a series of simulation results confirm the effectiveness and superiority of the proposed control strategy.

In this article, the problem of full state constraints for a class of uncertain nonlinear switched systems with time-varying delays under average dwell time is studied, and an adaptive event-triggered mechanism is proposed. The Lyapunov–Krasovskii function (LKF) is employed to solve the trouble caused by time-varying delays, neural networks are selected to approximate the uncertain terms in the system, and the state constraint problem is solved by constructing tan barrier Lyapunov function (Tan-BLF). What’s more, the constraint boundaries considered in this article can be expressed as functions that rely on time and historical information of the system. In addition, the mismatch behavior between subsystem and its controller is also considered. Finally, numerical simulation results verify the availability of the control strategy.

In this article, an adaptive tracking control problem is addressed for nonstrict-feedback stochastic nonlinear systems subject to state constraints. Fuzzy logic systems (FLSs) are used to model unknown nonlinearities and avoid the algebraic loop arising from the system structure. Appropriate integral Barrier Lyapunov functions (IBLFs) are chosen so that time-varying full state constraints can be guaranteed directly rather than by transforming the constraint object. In the framework of backstepping technology, the relative threshold strategy is introduced to modify the adaptive control scheme so that the controller can be updated only after the trigger condition has been met, which reduces the update frequency of the controller and the loss of the actuator. Combined with Lyapunov stability theory, it is shown that all closed-loop signals are bounded in probability, in which the states remain within the specified constraints, and there is no Zeno behavior. A series of simulation results are given to reveal the effectiveness of the constructed control scheme.

An adaptive fuzzy-based event-triggered control (ETC) scheme is putted forward in this article, targeted at multi-input multioutput switched nonlinear systems with the situation of control directions are unknown. By designing a state observer and by means of fuzzy logic system, the unmeasurable state and unknown nonlinear function is resolved. The command filter is introduced to backstepping design process, combine with the Nussbaum function technology, overcome the trouble of calculation explosion derived from the partial derivative of the virtual controller, and also solves the influence of unknown control directions on the controller design. By construct multiple Lyapunov functions and under the designed ETC mechanism, an adaptive ETC scheme is developed. In addition, the stability of the system is verified by combining the average dwell time method. Finally, a numerical simulation verification example is given.

Due to the unique self-acceleration, self-healing, and non-diffraction properties, Airy beams have been explored extensively and found applications in various fields. It has been proven as an essential aspect to tune the trajectory of Airy beams for extensive applications. In this paper, we propose a method based on liquid crystal (LC) alignment with metasurfaces, which enables dynamic tuning of the trajectory of Airy beams. Benefiting from both the tunable property of LCs and the compact alignment of metasurfaces, we achieve a sizeable linear potential in a short distance, which leads to the effective tuning of the trajectory of Airy beams dynamically. The introduction of metasurfaces into the alignment of LCs provides a promising method to manipulate the planar optical field.

In this article, an integral barrier Lyapunov-function (IBLF)-based adaptive tracking controller is proposed for a class of switched nonlinear systems under the arbitrary switching rule, in which the unknown terms are approximated by radial basis function neural networks (RBFNNs). The IBLF method is used to solve the problem of state constraint. This method constrains states directly and avoids the verification of feasibility conditions. In addition, a completely unknown control gain is considered, which makes it impossible to directly apply previous existing methods. To offset the effect of the unknown control gain, the lower bound of the control gain is added into the barrier Lyapunov function, and a regulating term is introduced into the controller. The proposed control strategy realizes three control objectives: 1) all the signals in the resulting system are bounded; 2) the system output tracks the reference signal to a arbitrarily small compact set; and 3) all the constraint conditions for system states are not violated. Finally, a simulation example is used to show the effectiveness of the proposed method.

This study examined the control problem triggered by an adaptive fuzzy event for uncertain fractional-order switched systems. First, an observer was designed to estimate unknown system states, and fuzzy logic systems were introduced to approximate unknown nonlinear functions. Furthermore, the dynamic surface technique is employed to solve complex differential problems of fractional-order switched systems. This article proposes a threshold-based event-triggered control scheme that avoids Zeno behavior to decrease the waste of communication resources. An event-triggered mechanism updates the control signal only when necessary to ensure system performance. The proposed control strategy enables all signals of a closed-loop system to be uniformly bounded and the tracking error of output has good convergence performance near the origin under an arbitrary switching mechanism. Finally, a numerical example is presented that demonstrates the effectiveness of the designed scheme.

In this article, a novel coverage algorithm is designed to realize dynamic tracking coverage of unmanned ground vehicles (UGVs) by unmanned aerial vehicles (UAVs) with quantity-adjustment behavior, which can adjust the quantity of UAVs to cover the moving UGVs. In traditional coverage control, the paths of UAVs are preplanned to achieve regional coverage. Additionally, the quantity of UAVs remains unchanged, resulting in a waste of resources and a lack of flexibility. To overcome these problems, we present a novel coverage algorithm. First, the tracking coverage algorithm is designed, in which the positions of the UGVs are sampled at a certain interval, and $k$ -means is used to obtain the optimal expected positions of UAVs. Then, to realize the quantity-adjustment behavior of UAVs, an evaluation mechanism is proposed to determine whether the UAVs should be added or withdrawn. UAVs should reach the expected positions during the sample interval. As a result, a finite-time terminal sliding-mode controller is designed to drive the UAVs to follow expected positions. Furthermore, we design a Lyapunov function to prove the stability of the controller. Finally, three simulation examples are put forward, and the results demonstrate the effectiveness of the proposed algorithm and controller.

A robust adaptive control method for a certain type of quarter active suspension system (ASS) is proposed in this work. The constraint issue of ASS is put into consideration primarily. Due to the limitation of the traditional barrier Lyapunov functions (BLFs), the integral barrier Lyapunov function (iBLF) is introduced to exert direct constraints on state variables in each stage under the backstepping frame, and neural networks (NNs) are applied to identify those unknown functions. Then, an adaptive law based on the projection operator is defined to eliminate the influence caused by the actuator failure. It is widely known that only the vertical displacement and velocity constraints are not violated, can the ASSs become stable and secure. It can be ultimately confirmed that all signals in the closed-loop system are bounded, and the control goals are satisfied. Last but not least, the feasibility of the approach is illustrated directly through a contrast simulation example.

In this paper, for a class of nonlinear cyber-physical systems (CPSs), the problem of adaptive security control for false data injection (FDI) attacks on the sensor and actuator is solved. Both sensor and actuator are destroyed by attackers, which makes the feedback control design unable to access the traditional error surface. Firstly, a state observer is constructed to mitigate the impact of sensor attacks. And then neural networks are used to approximate the nonlinear term and compensate for state-dependent actuator attacks. Finally, in order to reduce the impact of FDI attacks, we design a special time-varying symmetry barrier function in backstepping control design, which can achieve specific output signal constraints under FDI attacks. Through the above control strategy, the output constraint control problem of CPSs under FDI attacks is solved. Finally, a numerical simulation example demonstrates the effectiveness of the proposed controller.

This article presents the adaptive tracking control scheme of nonlinear multiagent systems under a directed graph and state constraints. In this article, the integral barrier Lyapunov functionals (iBLFs) are introduced to overcome the conservative limitation of the barrier Lyapunov function with error variables, relax the feasibility conditions, and simultaneously solve state constrained and coupling terms of the communication errors between agents. An adaptive distributed controller was designed based on iBLF and backstepping method, and iBLF was differentiated by means of the integral mean value theorem. At the same time, the properties of neural network are used to approximate the unknown terms, and the stability of the systems is proven by the Lyapunov stability theory. This scheme can not only ensure that the output of all the followers meets the output trajectory of the leader but also make the state variables not violate the constraint bounds, and all the closed-loop signals are bounded. Finally, the efficiency of the proposed controller is revealed.

This article addresses the adaptive tracking control problem for switched uncertain nonlinear systems with state constraints via the multiple Lyapunov function approach. The system functions are considered unknown and approximated by radial basis function neural networks (RBFNNs). For the state constraint problem, the barrier Lyapunov functions (BLFs) are chosen to ensure the satisfaction of the constrained properties. Moreover, a state-dependent switching law is designed, which does not require stability for individual subsystems. Then, using the backstepping technique, an adaptive NN controller is constructed such that all signals in the resulting system are bounded, the system output can track the reference signal to a compact set, and the constraint conditions for states are not violated under the designed state-dependent switching signal. Finally, simulation results show the effectiveness of the proposed method.

In this article, an adaptive fuzzy tracking control scheme based on a fuzzy state observer is proposed for a class of uncertain, nonstrict feedback, nonlinear systems with function constraints. In the first place, based on the approximation characteristic of fuzzy logic systems (FLSs), a fuzzy state observer is designed to estimate the immeasurable state variables in the controlled system. Next, under the framework of adaptive backstepping control technology, FLSs are selected not only to approximate unknown nonlinear functions but also to avoid the algebraic loop problem caused by nonstrict feedback structure. At the same time, asymmetric Barrier Lyapunov functions are selected to solve the problem that system states are subject to function constraints, which are related to states and time. Then, Lyapunov stability theory is utilized to prove the stability of the controlled system, the realizability of function constraints, and the convergence of output tracking errors. Finally, a simulation is given to check the effectiveness of the proposed control scheme.

In this article, the problems of state-triggered sampling and self-triggered tracking control for nonlinear systems with constraints are studied. First, the fuzzy observer is designed for the unknown state. In the presence of state-triggered sampling error, the cooperative design of constraint controller is a key problem to be solved. The median value theorem provides help to solve this problem and an asymmetric state-triggering strategy is presented. In addition, it is proved that the closed-loop signals are input-to-state stable, and the sampling error and tracking error are bounded. Then, a general self-triggered tracking control scheme is presented. In order to compare the control performance of different triggering mechanisms, fuzzy observer and logarithmic barrier Lyapunov function are selected also, and the scheme is designed under the framework of backstepping method. Co-designing controllers and scheduling functions is a key issue, so that the physical implementation benefits from not requiring constant monitoring of the state. Finally, the effectiveness of the proposed method is verified by case study, and the control performance of different triggering mechanisms is compared.

Cell migration occurs in confined microenvironments, which plays a vital role in the process of tumor metastasis. However, it is challenging to study their behaviors in vivo. Here we developed a cell squeeze system that can be scaled down to micrometers to mimic native physical confined microenvironments, wherein degrees of surface adhesion and mechanical constraints could be manipulated in order to investigate cell-migrating behaviors. Based on the microscale cell squeeze system, we found the synergistic role of lamin A/C and vimentin in cell transition and migration under strong confinement. The dynamic variations in lamin A/C and vimentin expression establish a positive feedback loop in response to confinement, effectively promoting amoeboid migration by modulating nuclear deformability while ensuring cell viability. This work shed light on modulating cell response to microenvironments by altering the expression of lamin A/C and/or vimentin, which may be a more efficient way of inhibiting cancer metastasis.

[Display omitted]

Before fertilization, sperms adhere to oviductal epithelium cells, and only a restrictive number of winner sperms can escape to reach the egg. To study the sperm escape behavior from the oviductal surface, we developed a microfluidic chip to fabricate an adhesive surface and to create a gradient of progesterone (P4) for mimicking the oviduct microenvironment in vivo. We identified three sperm motion patterns in such a microenvironment─anchored spin, run-and-spin, and escaped mode. By using kinetic analysis, we verified the hypothesis that the responsive rotation energy anchored with the adhered sperm head determines whether the sperm is trapped or detaching, which is defined as the hammer flying strategy of successful escape after accumulating energy in the process of rotating. Intriguingly, this hammer-throw escaping is able to be triggered by the P4biochemical stimulation. Our results revealed the tangled process of sperm escape before fertilization in the ingenious microfluidic system.

The proper regulation of transcription is essential for maintaining genome integrity and executing other downstream cellular functions1,2. Here we identify a stable association between the genome-stability regulator sensor of single-stranded DNA (SOSS)3and the transcription regulator Integrator-PP2A (INTAC)4–6. Through SSB1-mediated recognition of single-stranded DNA, SOSS–INTAC stimulates promoter-proximal termination of transcription and attenuates R-loops associated with paused RNA polymerase II to prevent R-loop-induced genome instability. SOSS–INTAC-dependent attenuation of R-loops is enhanced by the ability of SSB1 to form liquid-like condensates. Deletion of NABP2(encoding SSB1) or introduction of cancer-associated mutations into its intrinsically disordered region leads to a pervasive accumulation of R-loops, highlighting a genome surveillance function of SOSS–INTAC that enables timely termination of transcription at promoters to constrain R-loop accumulation and ensure genome stability.

In this article, an output feedback adaptive fuzzy tracking control method for a class of switched uncertain nonlinear systems with actuator failures and full-state constraints is proposed under an arbitrary switching signal combining the dynamic surface technique. Since the state variables of the system under study are not measurable, a fuzzy observer is constructed to identify the unmeasured states. The actuator failures are considered in the system. To compensate this failure, a fault-tolerant controller is proposed. Moreover, each state needs to be kept within the constraints, so the tangent Barrier Lyapunov function is selected to solve the full-state constraint problem, and the unknown nonlinear function is approximated by fuzzy-logic systems (FLSs). We also proved that all signals in the closed-loop system are bounded. Furthermore, the states can be kept within the predetermined range even if the actuator fails. Finally, a simulation example is given to verify the effectiveness of the proposed control strategy.

This article researches the adaptive fuzzy tracking control problem for uncertain nonlinear systems with multiple actuators and sensors faults. Compared with previous studies, all states of the system cannot be measured accurately in this article due to the existence of multiple sensors faults, and it brings significant difficulties to the design of the control scheme. Moreover, multiple actuators faults and external disturbance can also bring challenges to controller design. To solve these problems, we design different adaptive update laws to relieve the effects of unknown actuators faults, sensors faults, and external disturbance, respectively. Furthermore, the actual states can be estimated by combining sensors outputs with adaptive parameters. On this basis, the unknown nonlinear functions are approximated through the combining of fuzzy logic systems and the estimation of states. Then, a novel adaptive fuzzy tracking control algorithm is completed by the backstepping method. By employing the Lyapunov function, the presented novel fault-tolerant control algorithm can guarantee all signals of the system are bounded in spite of the occurrence of multiple faults. Finally, we verify the availability of the novel algorithm by comparing the control performance of the two algorithms.

This article presents an adaptive fuzzy fixed time time-varying formation control (TVFC) method for uncertain heterogeneous nonlinear multiagent systems (HNMASs) with full state constraints. Meanwhile, both partial loss of effectiveness and bias fault are considered in HNMASs. The fuzzy logic systems are selected as an effective tool to approximate uncertain nonlinear functions. The original constrained states of the systems will be converted to unconstrained states by the nonlinear transformed function. Compared with previous papers, it is the first time to handle the TVFC problem of HNMASs with full state constraints. In addition, formation control based on an adaptive fuzzy fixed time strategy not only ensures fast convergence of the system, but also the convergence time doesn't depend on any initial conditions. The stability of HNMAs is proven by the fixed time stability theory. Finally, a simulation is given to testify the effectiveness of the control method.

In this article, the problem of tracking control for a class of nonlinear time-varying full state constrained systems is investigated. By constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm, the intelligent controller and adaptive law are developed. Neural networks (NNs) are utilized to approximate the uncertain function. It is well known that in the past research of nonlinear systems with state constraints, the state constraint boundary is either a constant or a time-varying function. In this article, the constraint boundaries both related to state and time are investigated, which makes the design of control algorithm more complex and difficult. Furthermore, by employing the Lyapunov stability analysis, it is proven that all signals in the closed-loop system are bounded and the time-varying full state constraints are not violated. In the end, the effectiveness of the control algorithm is verified by numerical simulation.

This paper focuses on the adaptive neural control problem for a connected vehicles platoon system with external disturbances, actuator fault and constraints. Then, the tangent barrier Lyapunov function is introduced into the control design to solve the time-varying position and velocity constraints in a vehicle platoon. Since the selected engine dynamics model is nonlinear, the neural network is adopted to approximate the unknown internal vehicle dynamics. Moreover, the actuator fault effect is addressed by the designed adaptive law designed based on the projection operator. It can be eventually confirmed that all the involved closed-loop signals are bounded based on the Lyapunov stability theory. Finally, a comparative simulation shows the effectiveness of the proposed control scheme.

In this paper, an adaptive fuzzy control problem is studied for a connected automated vehicles platoon subject to unknown dead-zone input and constraints. To better handle the unknown nonlinear dynamical functions and disturbances, the nonlinear dynamics model is transformed to a new model. Then, the fuzzy logic system (FLS) is used to identify the unknown nonlinear functions. A dead-zone inverse technique is introduced to eliminate the negative effects of the unknown dead-zone input nonlinearity. In the framework of backstepping, the tangent barrier Lyapunov function (BLF) is introduced in this paper, and a distributed adaptive fuzzy control scheme is designed so that the position, velocity and acceleration of the vehicle platoon do not violate the given constrained boundaries. Finally, based on the Lyapunov stability theory, it is noted that all signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the proposed approach is validated by simulation results.

In this article, an adaptive tracking control method for the bionic active suspension system with the event-triggered mechanism is proposed. In order to achieve the ideal control objectives, a nonlinear suspension system structure is proposed based on the biological inspiration. In the existing research, the suspension system tracking control methods are only aimed at a single part of the mass, respectively. But this article adopts a novel tracking method for the vertical displacement difference, which can track the ideal reference model more accurately. For the sake of alleviating the problem of limited resources in the process of vehicle communication, a control method combined with the events triggering of the relative threshold is proposed. The design of the controller makes the vertical displacement and vertical moving speed of the bionic suspension system close to zero. It can effectively improve the communication efficiency between the actuator and the controller. In the design of this system, all the signals involved are bounded. The Zeno behavior is successfully avoided among the event-triggered control mechanism. Finally, the feasibility and rationality of this method are verified by the simulation analysis of the bionic suspension system.

In this article, the event-triggered control problem of the crane bridge system modeled by partial differential equations (PDEs) is mainly studied. Moreover, the uncertain system parameter is also considered, which is solved by using the adaptive approach. Then, during the design of the controller, an event-triggered mechanism is introduced to reduce resource consumption, reduce communication pressure, and increase the working capacity of the system. Furthermore, the stability of the system is analyzed and proved by a rigorous mathematical analysis and the Zeno phenomenon is successfully avoided. Finally, the effectiveness of the proposed control strategy is verified by simulation results.

In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to symmetric and asymmetric constraints are studied, respectively. Then, corresponding adaptive neural controllers are developed by virtue of backstepping design procedure and the learning ability of radial basis function neural network (RBFNN). It is worth mentioning that the integral Barrier Lyapunov function (IBLF), as an effective tool, is first applied to solve the above constraint problems. As a result, the state constraints are avoided from being transformed into error constraints via the proposed schemes. In addition, based on Lyapunov stability analysis, it is demonstrated that the errors can converge to a small neighborhood of zero, the full states do not exceed the given constraint bounds, and all signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in probability. Finally, the numerical simulation results are provided to exhibit the effectiveness of the proposed control approaches.

This paper proposes a leader–follower formation control protocol using fast finite-time (FFT) theory, based on second-order nonlinear multi-agent systems (MASs) with input saturation constraints. The artificial potential field method is addressed to implement the formation control with obstacle avoidance of the MASs. An adaptive FFT strategy is constructed that all the agents follow required formation performance. Neural networks are considered to approximate uncertain functions, which improved convergence and ensuring safety of distributed formation control. Finally, the validity of the theoretical approach is demonstrated by FFT stability theory validated by simulation examples.

ABSTRACTLiver fibrosis occurs following inflammation triggered by the integrated actions of activated liver-resident macrophages (Kupffer cells) and hepatic stellate cells (HSCs), and the multiplicity of these mechanisms complicates drug therapy. Here, we demonstrate that the selective bromodomain and extra-terminal (BET) bromodomain inhibitor compound38 can block both the Janus kinase-signal transducer and activator of transcription and mitogen-activated protein kinase signaling pathways in macrophages, which decreased their secretion of proinflammatory cytokines in a dose-dependent manner. The inactivation of macrophages attenuated lipopolysaccharide-induced injurious inflammation concurrent with a reduction in F4/80+ cells, proinflammatory cytokine levels, and neutrophil infiltration. Moreover, compound 38 inhibited the Wnt/β-catenin and transforming growth factor-beta/SMAD signaling pathways to abolish the activation of HSCs. In vivo, compound 38 significantly decreased the collagen deposition and fibrotic area of a CCl4-induced liver fibrosis model, and restored the deficiency of activated HSCs and the upregulation of liver inflammation. These results highlight the potential role of compound 38 in treating liver fibrosis considering its simultaneous inhibitory effects on liver inflammation and related fibrosis.

This letter is concerned with the attitude control of a quadrotor unmanned aerial vehicle (UAV) subject to the input constraint, attitude constraint and model uncertainty. Firstly, we construct an auxiliary system to eliminate the adverse impact of the input saturation. Secondly, we introduce the nonlinear state-dependent function to deal with the attitude constraint directly. Thirdly, the neural network is utilized to identify the unknown terms in the system. Finally, with the help of the backstepping technology, a fixed-time control scheme is presented, which guarantees that the desired signal is followed with the fixed-time convergent rate. The effectiveness of the proposed scheme is validated by a simulation verification.

Dear Editor, In this letter, a novel adaptive control design problem for uncertain nonlinear multi-input-multi-output (MIMO) systems with time-varying full state constraints is proposed, where the considered systems consist of various subsystems, and the states of each subsystem are interconnected tightly. It is universally acknowledged that in the existing researches with state constraints, system constraint bounds are always constants or time-varying functions. Different from previous methods, the constraint boundary of this letter is regarded as a special function of not only time but of state variables. In order to handle time-varying full state constraints, the tangent type time-varying barrier Lyapunov functions (tan-TVBLFs) are introduced. By combining neural networks (NNs) and backstepping technique, an intelligent controller is developed. Meanwhile, we introduce an even function to guarantee the feasibility of NN approximation of unknown functions over practical compact sets. The feasibility of the mentioned control strategy is certified through the simulation results.