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This paper concentrates on the secure consensus control problem for a class of second-order connected vehicle systems (CVSs) in the presence of denial-of-service (DoS) attacks. First, the necessary and sufficient conditions for the consensus of CVSs are derived in the attack-free case. Second, in order to defend against DoS attacks, we design a novel secure consensus control protocol where resilient predictors are used to estimate the states of other vehicles during DoS attacks. Then, by using Lyapunov stability theory, matrix analysis tool, and algebraic graph theory, the convergence of CVSs with the resilient predictors against DoS attacks is achieved in the attack case. It is proved that under the designed secure consensus control protocol with resilient predictors, not only the prediction errors can be converged to a bounded range, but also the system errors can be converged. Finally, the effectiveness of the proposed novel secure consensus control protocol with resilient predictors is illustrated by some simulation results.

Smart materials can dynamically react to environmental stimuli and thus change at the micro or macro scale. However, when exposed to harsh conditions, damage can occur to them to varying degrees, thus affecting their performance and service life. In this study, a recyclable shape memory polyurethane material with self-healing properties was prepared using polycaprolactone (PCL) and polytetrahydrofuran diol (PTMG) as raw materials and isofuranone diisocyanate (IPDI) as a cross-linking agent. Dynamic cross-linking networks are constructed by introducing reversible disulfide bonds to enhance the self-healing and recyclable properties of the materials. Hydroxylated multiwalled carbon nanotubes (MWCNTs-OH), as photothermal particles, impart a remote response ability to the material, making the material temperature variable under near-infrared radiation (NIR). The shape memory recovery of CNT-PCL-PTMG can be triggered remotely and precisely controlled the irradiated NIR area. The results of tensile tests, scanning electron microscopy (SEM) and optical microscopy (OM) show that 3%CNT-5PCL-5PTMG can achieve efficient and rapid self-healing. Additionally, the synergistic effect of transesterification and the dynamic exchange of disulfide bonds gives materials reprocessability for recycling use. The material is promising as an alternative material for soft robotics and smart sensors.

Alginate is a water-soluble and acidic polysaccharide derived from the cell wall and intercellular substance of brown algae. It is widely distributed in brown algae, such as Laminaria, Sargassum, and Macrocystis, etc. Alginate lyase can catalytically degrade alginate in a β-eliminating manner, and its degradation product-alginate oligosaccharide (AOS) has been widely used in agriculture, medicine, cosmetics and other fields due to its wide range of biological activities. This article is mainly to make a brief introduction to the classification, source and application of alginate lyase. We hope this minireview can provide some inspirations for its development and utilization.

In this paper, the H∞ control problem for a class of linear time-varying cyber-physical system (CPS) under randomly occurring hybrid attacks in a finite horizon is investigated. The hybrid attacks, including denial of service (DoS) attacks on both sensor-to-controller and controller-to-actuator communication channels and false data injection (FDI) attacks on sensors and actuators, aim to destroy the measurement data and control data in order to endanger the functionality of the closed-loop system. The purpose of this paper is to study the relationship between the attack injected signals and the controlled output, and to design the output feedback controller gains so that the H∞ performance of the closed-loop system is guaranteed over a given finite horizon, meanwhile, the impact of attack signals in the worst case on the linear quadratic performance can be reduced. In order to solve the above problems, both the methods of stochastic analysis and completing squares are utilized to establish the sufficient conditions for the existence of the desired controller, and a finite-horizon controller design algorithm is presented by solving two coupled backward recursive Riccati difference equations (RDEs) subject to some scheduled conditions. At last, the numerical simulation and experimental results are given out to demonstrate the effectiveness of the proposed approach.

To deal with energy crisis and environmental issues, higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made. Ecological cooperative adaptive cruise control (Eco-CACC) has been considered as an effective solution to decrease the fuel consumption and greenhouse gas emissions of a platoon of vehicles, and this article proposes an Eco-CACC strategy for a heterogeneous platoon of heavy-duty vehicles with time delays. The proposed Eco-CACC strategy consists of distributed control protocols for the following vehicles and a new model predictive controller for the leading one. Firstly, after the distributed control protocols are designed based on the neighboring vehicle information, the sufficient conditions that guarantee the internal stability and string stability are derived, and the upper bound of the time delays under controller parameters is also obtained. Secondly, assuming the vehicle platoon as a rigid body with followers staying at desired positions, the new model predictive controller is designed in order to further improve the overall fuel economy of vehicle platoon. Simulations are conducted to validate the sufficient conditions of internal stability and string stability, and to explore the fuel saving performance of the proposed control strategy. The simulation results demonstrate that, compared with the benchmark, the proposed Eco-CACC strategy can significantly improve the fuel economy of heterogeneous platoon.

This paper investigates the consensus problems of the third-order discrete-time Connected Vehicle Systems (CVSs) under cyber-attacks. First, the necessary and sufficient conditions for consensus of third-order discrete-time CVSs are derived in the absence of attacks by using algebraic graph and matrix theory. Then the interaction network framework between the original CVSs in the vehicle platoon layer and a virtual system in the hidden layer is established to resist cyber-attacks. Since sufficiently large attacks can be excluded from CVSs through a threshold defense mechanism, such potential attacks considered are bounded and generated by any linear or non-linear finite L2-gain exogenous dynamics system. It is proved that the stability of CVSs can be ensured and the state errors converge to a bounded range whether the attacks exist only in the vehicle platoon layer or in the overall systems (including the vehicle platoon layer and the hidden layer) by using the Lyapunov stability method. Finally, a simulation example containing several scenarios demonstrates the effectiveness and superiority of the proposed methods.

Broussonetia papyrifera (L.) L’Hér. ex Vent. has considerable economic and ecological value and a long history of use in China. In this paper, root cuttings were used as the material to establish an efficient vegetative propagation of B. papyrifera. The results revealed that root segments with a diameter of 1.5~2.0 cm and a length of 20~30 cm were most suitable for shoot regeneration, as these segments had the highest adventitious shoot induction rates (93.3%), strongest adventitious shoots, and highest multiplication coefficients (7.07). With regard to the methods used for root burial, a horizontal burial at a depth of 1~3 cm yielded the best results, in this case, the adventitious shoot induction rate can reach 86.7%. The best substrate combination was perlite: peat: coconut chaff = 1:1:1 (v/v/v), wherein the adventitious shoot induction rate can reach 75.6%. The best sterilization method was mixing soil with carbendazim and soaking the root sections in carbendazim for 30 min, wherein the adventitious shoot induction rate can reach 77.8%. Adding 0.2 mg/L naphthaleneacetic acid (NAA) to 1/4 Hoagland nutrient solution significantly improved the rooting rate of adventitious shoots to 82.2%, and the survival rate of the acclimatized plants was more than 90.0%.

This paper focuses on the data-driven optimal attack strategy against state estimation in cyber-physical systems (CPSs). Different from the research on attack strategies of specific attack types, the proposed attack strategy addresses the optimal selection of attacked targets, which can combine with different attack types and produce greater threats to CPS. In particular, a causality analysis (CA) on the measurement data is first proposed to evaluate the significance of nodes (sensor groups) and help the implementation of the optimal node attack, since the system topology and parameters are not available to adversaries. On the one hand, a multivariate transfer entropy and several data preprocessing methods are employed to complete the CA between sensor groups qualitatively. On the other hand, three new indexes, e.g., driver degree, are defined to complete the CA quantitatively. Moreover, the theoretical basis for the proposed node attack is provided, in which the superiority of the node attack is proven from the view of observability. Finally, the case studies on the smart grid are illustrated to verify the superiority of the proposed attack strategy.

State-of-charge (SoC) estimation is indispensable for battery management systems (BMSs). Accurate SoC estimation can improve the efficiency of battery utilization, especially for electric vehicles (EVs). Several kinds of battery SoC estimation approaches have been developed, but a simple and efficient method for battery SoC estimation that can adapt to a variety of lithium-ion batteries is worth exploring. To this end, a recurrent neural network (RNN) model based on a gated recurrent unit (GRU) is presented for battery SoC estimation. The GRU-RNN model can rapidly learn its own parameters by means of an ensemble optimization method based on the Nadam and AdaMax optimizers. The Nadam optimizer is used in the model pre-training phase to find the minimum optimized value as soon as possible, and then the AdaMax optimizer is used in the model fine-tuning phase to further determine the model parameters. To validate the effectiveness and robustness of the proposed method, the GRU-RNN model was trained and tested with three kinds of dynamic loading profiles and compared with existing SoC estimation methods. The experimental results show that the proposed method dramatically reduces the model training time and increases estimation accuracy.

The fuel consumption and greenhouse gas emissions can be reduced by organizing a group of vehicles into a platoon at a short inter-vehicular distance. Additionally, the eco-driving technology has the potential to further increase fuel efficiency by optimizing the speed trajectories of vehicles. However, little research has been done into the eco-driving of a vehicle platoon. This paper proposes a cooperative look-ahead control strategy for maximizing the fuel efficiency of vehicle platoon travelling on a road with varying slopes. In this paper, the aerodynamic drag, nonlinear engine fuel consumption model, discrete gear ratios, and three engine operating modes are considered in the control strategy; under system constraints, a cooperative optimal fuel consumption problem of vehicle platoon based on distributed model predictive control is formulated; to obtain the optimal solution of the formulated nonlinear optimization problem, after the minimum fuel consumption table and the corresponding optimal control variable tables are obtained, the nonlinear optimization problem with discrete control variables is transformed into a 0-1 binary mixed linear programming problem. Simulation results show that, compared with benchmarks, the proposed control strategy can significantly improve fuel efficiency under different passenger comfort requirements, allowed speed ranges, and predictive control horizons.

Micro-fractures are developed in volcanic layers of Cretaceous Yingcheng Fm in the deep part of Xujiaweizi fault depression, Songliao Basin. In the process of well drilling, various complex problems happen, such as borehole wall slabbing and collapse and serious fluid leakage. Based on conventional drilling fluid plugging evaluation methods, the real situations cannot be presented accurately, especially in fracture feature simulation and plugging effect evaluation. Therefore, a specific micro-fracture plugging evaluation method was put forward especially for high-temperature deep wells in the paper. It is a new type of micro-fracture core model with the fracture apertures in the range of 1–50 μm. It is made of aluminosilicate that is compositionally close to natural rocks. It is good in repeatability with fracture-surface roughness, pore development and fracture-surface morphology close to natural fractures. Obviously, this new model makes up for the deficiencies of the conventional methods. A new micro-fracture plugging evaluation instrument was independently designed with operating temperature of 200 °C and operating pressure of 3.5–5.0 MPa. It can be used to simulate the flow regime of downhole operating fluids, with the advantages of low drilling fluid consumption, convenient operation and low cost. The plugging capacity of the organo-silicone drilling fluid system was evaluated by using this instrument. It is shown that the grain size distribution of the drilling fluid is improved and its anti-collapse capacity is enhanced. Based on the field test in Well XSP-3, the safe drilling problems in volcanic layers with developed micro-fractures are effectively solved by using the drilling fluid formula which is optimized by means of this evaluation method. And the safe drilling is guaranteed in the deep fractured formations in this area.

In this paper, the strain rate dependence of hardening behavior of polycrystalline pseudoelastic (PE) and shape memory effect (SME) TiNi alloy under impact loading was investigated by experiments. Measurements of stress–strain curves, hardening modulus, hysteresis loop area, and temperature variation are synchronized using in situ infrared detector system at the strain rate range from 300/s to 2000/s. It is shown that with the strain rate increasing, for PE specimens, strain rate hardening is observed, while SME specimens perform a strong nonlinear strain hardening. The results of synchronous temperature measurement show that in stress-temperature space, for PE samples, the dynamic transformation path is strain rate independent, but for the SME samples, the opposite is true. Thermal-mechanical coupling does not seem to explain this difference, and hardening from microstructure variation should be considered for such difference.

The weighted state and relative state information feedback is introduced into the existing coordination control protocol aiming to improve the robustness of fractional-order multi-agent systems against fractional-order α . By applying frequency-domain method and Nyquist stability theorem, a sufficient condition is given to ensure the fractional-order coordination. It is shown that the proposed coordination control protocol can improve the robustness of fractional-order multi-agent systems against fractional-order under directed communication topology. Two numerical examples are given and the corresponding simulation results show that compared with the existed coordination control protocols, the proposed weighted state and relative state information feedback control protocol is feasible and effective for improving the robustness performance.

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs.

A commonly encountered problem in wireless sensor networks (WSNs) applications is to reconstruct the state of nature, that is, distributed estimation of a parameter of interest through WSNs' observations. However, the distributed estimation in autonomous clustered WSNs faces a vital problem of sensors' selfishness. Each sensor autonomously decides whether or not to transmit its observations to the fusion center (FC) and not be controlled by the fusion center (FC) any more. Thus, to encourage cooperation within selfish sensors, infinitely and finitely repeated games are firstly modeled to depict sensors' behaviors. Then, the existences of Nash equilibriums for infinitely and finitely repeated games are discussed. Finally, simulation results show that the proposed Nash equilibrium strategies are effective.

This paper considers the problem of source localization using quantized observations in wireless sensor networks where, due to bandwidth constraint, each sensor's observation is usually quantized into one bit of information. First, a channel-aware adaptive quantization scheme for target location estimation is proposed and local sensor nodes dynamically adjust their quantization thresholds according to the position-based information sequence. The novelty of the proposed approach comes from the fact that the scheme not only adopts the distributed adaptive quantization instead of the conventional fixed quantization, but also incorporates the statistics of imperfect wireless channels between sensors and the fusion center (binary symmetric channels). Furthermore, the appropriate maximum likelihood estimator (MLE), the performance metric Cramér-Rao lower bound (CRLB), and a sufficient condition for the Fisher information matrix being positive definite are derived, respectively. Simulation results are presented to show that the appropriated CRLB is less than the fixed quantization channel-aware CRLB and the proposed MLE will approach their CRLB when the number of sensors is large enough.

The problem of distributed estimation in energy-harvesting wireless sensor networks (EH-WSNs) is studied. In general, the energy state of an energy-harvesting sensor varies dramatically. Existing efforts mainly concentrate on the problem of distributed estimation for battery-powered WSNs, ignoring the crucial issue of energy harvesting. Therefore, the unpredictable energy harvesting, the energy storage device, and energy consumption are modeled in a unified way to jointly address the energy harvesting and distributed estimation problem. In this paper, combining with the classical adaptive distributed estimation scheme, the problem of parameter estimation in EH-WSNs is formulated as a game of complete and perfect information. Each player decides its strategy according to the others' energy states and actions. The subgame perfect equilibrium (SPE) is derived by backward induction. Simulation results show that the proposed SPE makes full use of the harvested energy and improves the estimation performance.

This paper is concerned with a reliable solution to the distributed estimation and control issues over hybrid two-tier industrial wireless sensor and actuator networks (IWSANs). It aims at applying wireless technology to industrial automation control domains in hash industrial environment. A main challenge in these application domains is that wireless communication channels must satisfy strict requirements of real time and reliability. Therefore, first, we propose a new interacting dual model (IDM) adaptive estimation algorithm to identify the unreliable wireless communication channels, and, second, to further enhance the accuracy and fault tolerance of state estimation for controlled plant, a federated multisensor estimation fusion approach is presented. Furthermore, considering that the IWSANs need commonly to be deployed over a vast geographical area, a distributed collaborative control strategy is adopted. Finally, an example of temperature control illustrates the effectiveness of the proposed method.