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This article addresses the adaptive optimized consensus tracking control problem of nonlinear multiagent systems (MASs) via a reinforcement learning (RL) algorithm. Specifically, the nonlinear high-order MASs are formulated in a canonical form, with considerations for both actuator effectiveness loss and time-varying bias faults. First, neural networks (NNs) are utilized to approximate unknown nonlinear dynamics, and a state identifier and a fault estimator based on NNs are established, both of which are essential for evaluating state information and bias faults, respectively. Second, to achieve a high-order canonical dynamic consensus and enhance the efficiency of the consensus control strategy, a sliding-mode mechanism is employed to regulate tracking errors. Moreover, we develop an adaptive NN-based fault-tolerant optimal control method by integrating the sliding-mode mechanism with an actor–critic structured RL algorithm. It is proved that the outputs of the MASs precisely align with the desired reference signals, while ensuring the boundedness of all closed-loop signals. Finally, the proposed control methodology's effectiveness is validated through a simulation example.

This brief investigates the security control problem of interconnected nonlinear power systems (INPSs) involving the wind farm subjected to actuator deception attacks. A dynamic event-observer-based sliding mode controller is presented to guarantee the stability of the INPSs. First, a novel memory-based integral composite observer is presented to estimate the unknown variables, including lumped disturbances, deception attack functions and partial states of the power system. In this estimation scheme, a dynamic event condition is designed to achieve the transmission efficiency. Then, based on the estimation outputs, an observer aided sliding mode manifold is designed, and the stability of the overall INPSs is analyzed after the trajectory of the power system reach onto the manifold. Moreover, a security controller is designed to ensure the reaching ability and to recover the system performance with the help of the estimation values. Finally, the compensation ability of the presented method is verified by a class of two area power systems.

Device-to-device (D2D) communication, as a traffic offloading technology in the fifth-generation (5G) network, can be widely used in several scenarios to provide 5G characteristics of higher speed, lower latency, and larger capacity. D2D data transmission over wireless channels among mobile devices is vulnerable to security threats and privacy violations from third parties and relay nodes. However, academia and industry have yet to propose relevant schemes or standards for D2D relay data transmission scenarios. We first propose a generic construction for D2D relay communication in this paper. Then, a concrete anonymous and secure D2D data transmission scheme with trajectory tracking is presented based on Chebyshev polynomials, hash-based message authentication code, and symmetric encryption. We employ a formal verification tool - Tamarin, modal logic analysis - BAN logic, and informal security analysis to demonstrate the security features of the proposed scheme. The performance evaluation shows that the proposed scheme can achieve desirable efficiency compared with other related schemes. Finally, we developed an APP, ‘D2DWatchmen’, to simulate the whole protocol and test its robustness and real execution time, where the result shows good availability and effectiveness.

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 brief investigates the problem of secure state estimation (SSE) for cyber-physical systems (CPSs) which communicate through multiple channels under denial of service (DoS) attacks, where the considered DoS attacks occur aperiodically and independently in all channels. The only limitation for DoS attacks is that the duration of each attack is bounded. First, a secure transmission strategy is given to transmit signals and resist DoS attacks. Then, a novel fixed-time secure state estimation (FTSSE) method is proposed to ensure that the SSE for CPSs is realized within a predetermined fixed-time based on the developed secure transmission strategy. In addition, the accurate state values of some moments can be obtained at a given delay time. The sufficient condition and the proof of effectiveness for the FTSSE method based on secure transmission strategy are given through theoretical analysis. Finally, the results of an example simulation are given to verify the effectiveness of the designed method.

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 paper presents a reaction kinetics model for analyzing the coal tar hydrodeoxygenation process from the perspectives of deoxygenation, hydrogenation, and cracking. The model is established based on the data from 24 sets of coal tar hydrotreatment experiments performed at different temperatures, pressures, and liquid hourly space velocity (LHSV) in a fixed-bed reactor. In the established model, the reaction kinetics equations, mass transport equations, and property calculation equations are coupled and solved simultaneously. This model can predict the distribution of H2partial pressure, H2O partial pressure, and the concentrations of oxygen-containing compounds, aromatics, and cycloalkanes along the reactor axis, under different conditions (temperature, pressure, and space velocity). The validation experiments show that the model's prediction error for hydrodeoxygenation reaction product concentration is no more than 1.1%, for hydrogenation reaction product concentration is within 6.5%, and for cracking reaction product concentration is within 8.5%.

In this paper, the authors propose an adaptive Barrier-Lyapunov-Functions (BLFs) based control scheme for nonlinear pure-feedback systems with full state constraints. Due to the coexist of the non-affine structure and full state constraints, it is very difficult to construct a desired controller for the considered system. According to the mean value theorem, the authors transform the pure-feedback system into a system with strict-feedback structure, so that the well-known backstepping method can be applied. Then, in the backstepping design process, the BLFs are employed to avoid the violation of the state constraints, and neural networks (NNs) are directly used to online approximate the unknown packaged nonlinear terms. The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero. Meanwhile, it is shown that the constraint requirement on the system will not be violated during the operation. Finally, two simulation examples are provided to show the effectiveness of the proposed control scheme.

This study investigates the synergistic effect during the co-hydrogenation process of coal tar and biomass oil through experiments. By conducting hydrogenation experiments on model compounds of coal tar (naphthalene, tetrahydronaphthalene, decalin, and 9,10-phenanthrene) and waste soybean oil, it was found that partially saturated and fully saturated polycyclic aromatic hydrocarbons release active hydrogen atoms due to dehydrogenation at high temperatures. These active hydrogen atoms can promote the hydrogenation conversion of fatty acids derived from triglycerides in waste soybean oil during the co-hydrogenation process with coal tar. Through co-hydrogenation experiments of coal tar and waste soybean oil, it was discovered that when coal tar was first hydrogenated under conditions of temperature of 360 °C, pressure of 8 MPa, and LHSV (liquid hourly space velocity) of 0.4 h−1, and then mixed with waste soybean oil (at a concentration of 30 g/100 mL) for hydrogenation under the conditions of temperature of 360 °C, pressure of 8 MPa, and LHSV of 0.4 h−1, the deoxygenation conversion rate of fatty acids in the soybean oil is 94.5%. The hydrogenation deoxygenation conversion rate of the product in this process was 87.5% higher than that of individual fatty acids. This study indicates that the co-hydrogenation of hydrogenated coal tar and bio-oil promotes the deoxygenation conversion of bio-oil.

The present study focuses on investigating the microstructural evolutions and mechanical properties of energetic Al1(TiZrNbTaMoCr)15refractory high-entropy alloys with the different heat treatments at low Al content state. It is found that even with a reduction in the Al content, the strength of these alloys remains unaffected at room temperature and high temperature, while the plasticity improves significantly. In particular, the coherent BCC/B2 microstructure with needle-like B2 nanoprecipitates dispersed into the BCC matrix is formed in 873 K-aged Al1Ti6Zr2Nb3Ta3Mo0.5Cr0.5alloy. Therefore, this alloy exhibits the highest compression yield strength (σYS= 1333 and 717 MPa) at room temperature and 1073 K, respectively. After 973 K-aged, the coherent BCC/B2 microstructure underwent destabilization, and the B2 phase transforms into the brittle Zr5Al3phase which then coarsens and dominates the microstructure of S3-AlTa3alloy after 1073 K-aged. Moreover, these current alloys exhibit exceptionally high theoretical exothermic enthalpy (ΔH), surpassing 11606 J·g−1, which highlights their significant potential as innovative high-performance energetic structural materials.

The adsorption and activation of pollutant molecules and oxygen play a critical role in the oxidation reaction of volatile organic compounds (VOCs). In this study, superior adsorption and activation ability was achieved by modulating the interaction between Pt nanoparticles (NPs) and UiO-66 (U6) through the spatial position effect. Pt@U6 exhibits excellent activity in toluene, acetone, propane, and aldehyde oxidation reactions. Spectroscopic studies, 16O2/18O2kinetic isotopic experiments, and density functional theory (DFT) results jointly reveal that the encapsulated Pt NPs of Pt@U6 possess higher electron density and d-band center, which is conducive for the adsorption and dissociation of oxygen. The toluene oxidation reaction and DFT results indicate that Pt@U6 is more favorable to activate the C–H of toluene and the C═C of maleic anhydride, while Pt/U6 with lower electron density and d-band center exhibits a higher oxygen dissociation temperature and higher reactant activation energy barriers. This study provides a deep insight into the architecture–performance relation of Pt-based catalysts for the catalytic oxidation of VOCs.

Bubbling fluidized-bed biomass fast pyrolysis is a crucial technology for carbon neutrality and sustainability, and computational fluid dynamics (CFD) is one of the promising approaches to investigate and optimize bubbling fluidized-bed biomass fast pyrolysis. However, traditional CFD is still computationally costly for bubbling fluidized-bed biomass fast pyrolysis, especially for spatiotemporal transport-reaction behaviors, which are critical to clarifying intrinsic characteristics and optimizing operations. To address this issue, a deep learning (DL) model centered on convolutional neural networks was developed based on CFD results to efficiently predict spatiotemporal distributions of quantities of each phase in a bubbling fluidized bed for biomass fast pyrolysis. Input of the DL model is a sequence of spatiotemporal distributions, and only an initial input is required to generate continuous outputs. The model was optimized by adjusting four typical parameters, i.e., length of input sequence, number of neurons, learning rate, and prediction step size. Accuracy of short-term prediction (10 frames) and stability of long-term prediction (1000 frames) were analyzed as well as the relationship between time-averaged distributions and prediction length. It was found that with satisfactory accuracy, several orders of magnitude increase in computation efficiency can be realized. Thus, the developed model paves the way for low-cost and high-accuracy simulations of biomass fast pyrolysis.

In this article, two novel adaptive fault-tolerant control schemes for a class of nonlinear strict-feedback cyber–physical systems (CPSs) with deception attacks are presented. Deception attacks, such as false data-injection attacks, which destroy sensor networks, make the outputs and states of the CPSs unavailable. It is very difficult and challenging for a designer to achieve the tracking control under the circumstance of cyberattacks. To realize the tracking control for the studied CPSs, we propose a new coordinate transformation technology without precedent, where it takes the attack gains into account and uses the compromised states to design the corresponding controllers. In the backstepping design process, Nussbaum functions are presented to alleviate the influence of the unknown attack gains. Furthermore, we consider the actuator faults problem, which includes the loss of effectiveness and the bias fault. By skillfully designing the adaptive laws, the effect of actuator faults is completely eliminated. It is theoretically proved that the first proposed tracking control scheme can guarantee all signals in the closed-loop system are bounded and the output can track the desired reference signal. In addition, the second adaptive control scheme is also developed for the CPSs under the actuator faults and a more general assumption on the deception attacks is proposed simultaneously. Finally, the feasibility of the new proposed methods is verified by MATLAB simulation analysis.

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.

Nitrous oxide (N2O) has a detrimental impact on the greenhouse effect, and its efficient catalytic decomposition at low temperatures remains challenging. Herein, the cobalt-based high-entropy oxide with a spinel-type structure (Co-HEO) is successfully fabricated via a facile coprecipitation method for N2O catalytic decomposition. The obtained Co-HEO catalyst displays more remarkable catalytic performance and higher thermal stability compared with single and binary Co-based oxides, as the temperature of 90% N2O decomposition (T90) is 356 °C. A series of characterization results reveal that the synergistic effect of multiple elements enhances the reducibility and augments oxygen vacancy in the high-entropy system, thus boosting the activity of the Co-HEO catalyst. Moreover, density functional theory (DFT) calculations and the temperature-programmed surface reaction (TPSR) with isotope labeling demonstrate that N2O decomposition on the Co-HEO catalyst follows the Langmuir–Hinshelwood (L-H) mechanism with the promotion of abundant oxygen vacancies. This work provides a fundamental understanding of the synergistic catalytic effect in N2O decomposition and paves the way for the novel environmental catalytic applications of HEO.

Power transformer emits continuous vibration signals during working, which contain plenty of impulses and fluctuations caused by mechanical faults, and these signals are the main data source to evaluate the operational condition of a power transformer. This study researches the voiceprint features of vibration sound signals within power transformers under different operational conditions and constructs an automatic and noninvasive condition monitoring and fault diagnosis system based on acoustic characteristics. First, a normal/faulty operational condition classification module is proposed using acoustic short-time energy and zero-crossing rate to evaluate the real-time operational condition of a power transformer. Second, a multifault recognition module with finite signal samples is constructed based on the time–frequency characteristics of acoustic signals, and an automatic fault diagnosis system for power transformers is built. To improve the accuracy and robustness under dynamic noisy environments and complicated operational conditions, a multidimensional performance optimization method is proposed to handle the problems caused by environmental noises, mixed faults, and unknown faults. Experimental results demonstrate that the proposed condition monitoring and fault diagnosis system can fast distinguish the faulty operational conditions and diagnose six types of typical faults of power transformers, as well as mixed faults, under different noisy environments with an accuracy of more than 97.83%.

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.

An adaptive event-triggered control strategy for non-strict feedback nonlinear cyber-physical systems with time delays against deception attacks and actuator faults is presented. The most prominent difficulty lies in after the system signals are damaged by malicious deception attacks, all the exact states in the system are unavailable. Another design difficulty is the common existence of unknown nonlinearities and time-varying time delays, which makes it difficult to get the desired controller. Thus, to stabilize the studied cyber-physical systems, an unusual coordinate transformation is presented, where the attack gains and the problem of unavailability of states are considered simultaneously. Furthermore, the effect of the malicious deception attacks on the studied system is tackled by using Nussbaum technology and designing the Lyapunov functions with the attack gains. The unknown nonlinear functions, the mismatch problem for the control input caused by the nonlinearities of time-varying time delays are processed by neural networks technology and Lyapunov-Krasovskii functions. As a result, a controller based on event-triggered mechanism is constructed to reduce the waste of communication resources. The presented control strategy can ensure the signals in the closed-loop system are bounded. Finally, the Matlab simulation experiments are showed to verify the effectiveness of the developed strategy. Note to Practitioners—This paper studies the adaptive event-triggered control strategy for non-strict feedback nonlinear cyber-physical systems with time delay under deception attacks and actuator faults. Cyber-physical systems are widely used in power system, aviation system and other practical systems. However, due to the openness of network communication channels, attackers are prone to transmitting incorrect data to controllers and actuators, resulting in unusable system states, which is a challenging problem to ensure system stability by only using the compromised system states. In addition, the safety control problem of cyber-physical systems with time delays and actuator faults is investigated simultaneously, which poses significant challenges to the design of an desired controller. Therefore, by constructing Lyapunov functions and designing adaptive laws, not only the system under deception attacks is stabilized, but also the control strategy studied is made more practical.

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.

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.

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.

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 explores the practical finite/fixed-time consensus control algorithms for nonlinear multiagent systems (MASs) using an initial-value-free nonlinear mapping (NM) approach. By integrating a piecewise continuous function into each virtual control, the novel control signal can effectively avoid the singularity problem inherent in finite-time and fixed-time work. Compared with the previous constrained papers, we reconstruct the nonlinear MASs by introducing boundary performance functions into the NM method to release the initial-value conditions. Then, a command-filtered technique is provided to tackle the issue of the “explosion of complexity” stemming from iterative differentiation regarding the virtual controls, while a twice-transformation design technique is used to compensate for the filtered error. The adaptive controllers utilizing radial basis function neural networks (RBF NNs) can effectively address the effects of the unknown control gains and system uncertainties. The explored control strategies ensure that all signals of the closed-loop system converge into a compact set near zero within a practical finite/fixed time without violating the imposed constraints. Ultimately, the simulation outcomes on robotic systems illustrate the availability of the designed algorithms.

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.

Controller Area Network (CAN) is the most wide-used bus system in Intra-Vehicle Networks(IVN). However, the nature of broadcast communication and the lack of security mechanisms make the CAN bus extremely fragile against malicious attacks. Although there are works protecting IVN, most of them are not feasible when applied to real vehicles because they do not consider the IVN node capability. In this paper, we propose a security framework for the CAN bus, covering ECU entity identity management and authentication, symmetric key generation and update, intra-domain, cross-domain secure transmission, and sensitivity-based security classification methods. We formally verify our protocols using the up-to-date tool Tamarin and simulate real attacks in a simulation environment and the results show that the proposed protocol can resist these attacks. By the use of speck encryption and the Chaskey MAC algorithm in our schemes, the analysis results show that the increased time of a frame for a single ECU in our proposed intra-domain scheme is $2.09 ms$ to $2.78 ms$ on Arduino Mega, and $121.65 \mu s$ to $152.15 \mu s$ on Arduino DUE, which takes up 6.08% to 7.61% of a 10ms cyclic time frame. And in the cross-domain scheme is $2.55 ms$ to $3.24 ms$ on Arduino Mega, and $134.30 \mu s$ to $164.80 \mu s$ on Arduino DUE, which takes up 6.72% to 8.24% of a 10ms frame. To the best of our knowledge, this is the first time an IVN cross-domain secure transmission protocol has been proposed without changing the IVN network topology or the CAN protocol. Our work brings practical protection to IVN.

In this article, for the single-input–single-output (SISO) nonlinear strict-feedback system, optimized backstepping (OB) control combined with the dynamic surface (DS) technique is developed. OB is to make every subsystem control of backstepping as the optimized one so as to ensure the entire backstepping control being optimized. However, the original design of OB still needs to repeatedly calculate the derivative of virtual controls, as a result, it will inevitably cause the problem of “differential explosion.” In order to alleviate the phenomenon, the OB control is combined with the DS technique. Furthermore, OB control needs to conduct with reinforcement learning (RL) in every backstepping step, hence simplifying the algorithm of RL is very necessary and substantive for achieving the combination. In this work, because the optimized control derives both critic and actor training laws by utilizing a simple positive function instead of the square of approximation of Hamilton–Jacobi–Bellman (HJB) equation, it can obviously simplify the RL algorithm to compare with the traditional optimizing methods. Finally, the feasibility is illustrated via both theory and simulation.

In this brief, the problem of adaptive bipartite tracking control for nonlinear multi-agent systems with unmatched disturbances and backlash-like hysteresis is investigated. Cooperative and competitive relationships in multi-agent systems are taken into account, which represents a frequently occurring situation. To decrease unnecessary data transmissions, an innovative distributed adaptive self-triggered control protocol is proposed. In contrast to the traditional self-triggered control strategy, the scheme designed in this brief can adaptively adjust the trigger intervals according to system performances. Furthermore, a command filter is introduced into the design process to solve the problem of complexity explosion. The effect of backlash-like hysteresis is successfully compensated by the designed controller. Finally, a practical example is implemented to demonstrate the effectiveness of the proposed scheme.

Purpose: To explore the mechanism of insulin secretion dysfunction in pancreatic beta cells induced by N-glycosylation mediated by an infection from the hepatitis C virus (HCV). Methods: Min6 cell models infected with HCV and stimulated with glucose were constructed. Meanwhile, an HCV-infected animal model and a type 2 diabetes mellitus (T2DM) rat model were constructed. Glucose uptake in the Min6 cells was detected, and insulin secretion was detected by ELISA. Flow cytometry, immunofluorescence staining, Western blotting, RT-qPCR, and lectin blotting were used to detect the expression levels of related proteins and mRNA, as well as the level of N-glycosylation. HE staining was used to observe the pathological changes in the pancreatic tissue, and an oral glucose tolerance test (OGTT) was used to evaluate the glucose tolerance of the rats. Results: Compared with the NC group, the expression levels of GnT-IVa, GLUT2, galectin-9, and voltage-dependent calcium channel 1.2 (Cav1.2) were significantly downregulated in the HCV-infected group. The ATP-sensitive potassium channel (KATP) component proteins SUR1 and Kir6.2 were significantly upregulated, while intracellular glucose intake and insulin secretion decreased, N-glycosylation levels and ATP levels significantly decreased, and the overexpression of GnT-IVa reversed the effect of the HCV infection. However, treatment with the glycosylation inhibitor kifunensine (KIF) or the KATPchannel activator diazine (Dia) reversed the effects of the overexpression of GnT-IVa. In the animal experiments, HE staining revealed serious pathological injuries in the pancreatic tissue of the HCV-infected rats, with decreased glucose tolerance and glycosylation levels, decreased insulin secretion, downregulated expression of GnT-IVa, GLUT2, and Cav1.2, and upregulated expression of SUR1 and Kir6.2. The overexpression treatment of GnT-IVa or the KATPchannel antagonist miglinide reversed the effects of HCV. Conclusion: HCV infection inhibits GLUT2 N-glycosylation on the pancreatic β cell surface by downregulating the expression of GnT-IVa and then activates the KATPpathway, which ultimately leads to disturbances in insulin secretion.

This article proposes a decentralized optimal zero-sum game method for nonlinear interconnected systems with asymmetric input constraints and external disturbances based on a data- and event-driven approach. First, by assigning a particular cost function for each isolated subsystem with disturbances, the decentralized control problem is equivalently converted into finding a sequence of optimal zero-sum game schemes. Then, a recurrent neural network-based data-driven model is designed to reconstruct the system, ensuring that the acquired results are applicable in a broad range of contexts. After obtaining the system model, an event-triggered adaptive dynamic programming algorithm is used to approximate the Nash equilibrium solutions of Hamilton–Jacobi–Isaacs equations under a critic architecture. A noteworthy characteristic is that the critic updating laws are designed via an experience replay technology, such that the persistence of excitation condition can be excluded. Finally, all signals of the close-loop system are guaranteed to be uniformly ultimately bounded based on the Lyapunov stability theory, and a simulation example is presented to exemplify the effectiveness of the proposed decentralized optimal zero-sum game scheme.

The Class-1 Internet of Things (IoT) device is difficult to protect because of the lack of minimal hardware and extension support. Researchers have proposed several remote attestation schemes to protect IoT devices. However, hardware-assisted remote attestation methods require the support of minimal hardware, which is not suitable for Class-1 IoT devices. And regular software-based methods can only verify the integrity rather than the identity of devices. In this article, we propose a new identity authentication scheme for Class-1 IoT devices based on clock skew, achieving not only identity authentication but also device integrity verification. The formal verification results by employing the Tamarin tool show that our protocol achieves mutual authentication, man-in-the-middle attack resistance, replay attack resistance, and counterfeit attack resistance. And the proposed authentication protocol is evaluated on a small cluster of Arduino Mega boards, achieving 100% accuracy of identification. Our work combines remote attestation and device fingerprint and is fit for Class-1 IoT devices.

In this article, the resilient adaptive intermittent output feedback control is investigated for a class of uncertain strict-feedback nonlinear systems under unknown sensor deception attack. The underlying problem is rather challenging not only because the system contains mismatched uncertainties, the output information is corrupted by an additional attack signal, and only the intermittent false output is available for control design, but also because the virtual control terms are nondifferentiable due to the utilization of event triggering. To circumvent these obstacles, we construct a new fuzzy state estimator via intermittent false output and fuzzy-logic systems (FLSs), which, in conjunction with a new set of error surfaces employing the false and estimated state information, allows a novel observer-based resilient adaptive intermittent control strategy to be developed. The results of the proposed resilient adaptive output event-triggered control shown that the stabilization errors converge to a vicinity of the origin, and all the signals of the overall system remain bounded. Numerical simulations confirm the efficiency of the proposed methodology.

This brief deals with the command filter-based adaptive control problem of constrained nonlinear cyber-physical systems (CPSs) under the deception attacks through sensor and actuator networks. Firstly, an improved coordinate transformation technique considering the available compromised states and the outputs of command filters is proposed, which can be combined with barrier Lyapunov functions (BLFs) to design the corresponding controllers and achieve full-state constraints. Then, to handle the unknown time-varying control gains of the actuator caused by the deception attacks, the rational adaptive laws are cleverly designed for the upper bounds involving the time-varying control gains. Furthermore, by introducing the command filtering technology and the event-triggered strategy, the control scheme for the nonlinear CPSs is proposed to guarantee the boundedness of the whole closed-loop signals and reduce communication resources. Finally, the simulation results demonstrate the availability of the developed control method.

This article solves the cooperative adaptive tracking control problem for nonlinear pure-feedback multi-agent systems (MASs). Compared with the previous achievements of adaptive control of pure-feedback MASs, the partial derivative of the nonaffine function may not exist by using decoupling technology. In the controller design framework based on the backstepping technique, the additional state variables are processed using the special properties of the radial basis function neural networks (RBF NNs). A special-shaped Laplacian matrix is proposed to unify the leader gain form in the tracking error design process (the coefficient in the second term of tracking error). Furthermore, an event trigger mechanism (ETM) is introduced to save resources. The constructed controller under the ETM can not only stabilize the system states but also make the tracking error reach a small accuracy. Finally, the simulation results demonstrated the feasibility of the proposed method.

This study investigated the correlation between the MnS·xMnO·yAl2O3·zSiO2·TiN complex inclusions and localized corrosion initiation of the wire arc additively manufactured (WAAMed) super duplex stainless steel (SDSS) part. The results showed that partial dissolution of crystalline MnS within the complex inclusions generated an acidic aggressive environment to led the dissolution of amorphous oxides part, thereby triggering the process of localized corrosion. Additionally, micro-crevices between the inclusions and metallic matrix exacerbated localized corrosion. Finally, the catalytic-occluded cells accelerated propagation of pits. Hence, formation of high density of inclusions resulted in the poor localized corrosion resistance and repassivation ability of the as-built WAAMed SDSS.

Broadening the understanding of the anisotropic behavior of super duplex stainless steel (SDSS) fabricated by wire arc additive manufacturing (WAAM) can provide valuable insights for designing and optimizing WAAM components. The purpose of this study is to investigate the anisotropic behavior between mechanical properties, corrosion resistance, microstructure and crystallographic features of wire arc additive manufactured (WAAMed) SDSS thin-wall component, by means of optical microscopy (OM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), X-ray diffraction (XRD), hardness testing, tensile testing and electrochemical corrosion testing. Meanwhile, a control sample set prepared from commercial hot-rolled SAF 2507 SDSS was also investigated for comparison. The results show that the mechanical properties and corrosion behaviors of WAAMed SDSS are anisotropic, which are attributed to the nonequilibrium phase ratio and anisotropic crystal features in the as-built WAAMed SDSS affected by the repeated thermal cycles inherent to WAAM process.

Information security and user comfort are the games that smartphone manufacturers have always had to face. Explicit authentication (EA) methods, including password, fingerprint recognition, and face recognition, have become the most popular ways to unlock smartphones. However, these methods also incur some troubles in users’ daily lives and even suffer from security problems like shoulder surfing attacks and password reuse attacks. Unlike the EA methods, the implicit authentication (IA) methods employ the user’s behavior, posture, and so on to confirm who the user is. The “Smart Lock (SL),” as a new IA introduced by the biggest smartphone system manufacturer, Google, has effectively increased user comfort while ensuring user information security. However, we found that the SL is not secure enough, where anyone wearing a smart device can be authenticated. In this article, based on the “Trusted Devices (DEVICE)” approach in SL, we design a band with sensors commonly found on smart wearables and two authentication models to improve the security of SL scenarios. We use our designed band to collect data to evaluate our models. The experimental results show that our designed band is universal. Our authentication model for power-constrained devices such as wearables has 97.01% accuracy and power overhead of 0.0195 mAh per time. The designed authentication model for smartphones has an accuracy of 99.67% and power consumption of 0.83 mAh per time. Therefore, our scheme can meet the IA for different security requirements in different scenarios.

This paper investigates the stochastic stability of discrete time positive Markov jump nonlinear systems (PMJNS). Some definitions on stochastic stability for discrete time PMJNS are introduced first. Then, using the multiply max-separable Lyapunov function method, some stochastic stability criterions of discrete time PMJNS are provided, and some corresponding criterions are also provided for discrete time positive Markov jump linear systems (PMJLS). Different from previous conclusions that require subsystems to be stable or marginally stable, the obtained results allow some subsystems to be unstable. Based on the proposed criterions, the stochastic stability behavior of discrete time positive Markov jump systems can be obtained just from the algebraic properties of the system function and the probability characteristics of the Markov chain. To illustrate the main results, two simulation examples are provided at the end.