Consensus is one of the fundamental distributed control technologies for collaboration in multi-agent systems such as collaborative handling in intelligent manufacturing. In this paper, we study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact, and achieving the resilient average consensus. General types of misbehaviors are considered, including attacks, accidental faults, and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection compensation based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios wherein information sets are intermittently available due to link failures, a stochastic extension named stochastic detection compensation based consensus (S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient accurate average consensus and unbiased resilient average consensus in a statistical sense, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy of S-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithms.

This paper is concerned with the scaled formation control problem for multi-agent systems (MASs) over fixed and switching topologies. First, a modified resilient dynamic event-triggered (DET) mechanism involving an auxiliary dynamic variable (ADV) based on sampled data is proposed. In the proposed DET mechanism, a random variable obeying the Bernoulli distribution is introduced to express the idle and busy situations of communication networks. Meanwhile, the operation of absolute value is introduced into the triggering condition to effectively reduce the formation error. Second, a scaled formation control protocol with the proposed resilient DET mechanism is designed over fixed and switching topologies. The scaled formation error system is modeled as a time-varying delay system. Then, several sufficient stability criteria are derived by constructing appropriate Lyapunov–Krasovskii functionals (LKFs). A co-design algorithm based on the sparrow search algorithm (SSA) is presented to design the control gains and triggering parameters jointly. Finally, numerical simulations of multiple unmanned aerial vehicles (UAVs) are presented to validate the designed control method.

In this paper, the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems. The dynamical model of each agent is unknown and only the input/output data are available. A model-free adaptive control method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent. Then, by means of the distributed gradient descent method, a novel event-triggered model-free adaptive distributed optimization algorithm is put forward. Sufficient conditions are established to ensure the consensus and optimality of the addressed system. Finally, simulation results are provided to validate the effectiveness of the proposed approach.

This article addresses the secure finite-time tracking problem via event-triggered command-filtered control for nonlinear time-delay cyber physical systems (CPSs) subject to cyber attacks. Under the attack circumstance, the output and state information of CPSs is unavailable for the feedback design, and the classical coordinate conversion of the iterative process is incompetent in relation to the tracking task. To solve this, a new coordinate conversion is proposed by considering the attack gains and the reference signal simultaneously. By employing the transformed variables, a modified fractional-order command-filtered signal is incorporated to overcome the complexity explosion issue, and the Nussbaum function is used to tackle the varying attack gains. By systematically constructing the Lyapunov–Krasovskii functional, an adaptive event-triggered mechanism is presented in detail, with which the communication resources are greatly saved, and the finite-time tracking of CPSs under cyber attacks is guaranteed. Finally, an example demonstrates the effectiveness.

This paper investigates the problem of outlier-resistant distributed fusion filtering (DFF) for a class of multi-sensor nonlinear singular systems (MSNSSs) under a dynamic event-triggered scheme (DETS). To relieve the effect of measurement outliers in data transmission, a self-adaptive saturation function is used. Moreover, to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization, a DETS is adopted to regulate the frequency of data transmission. For the addressed MSNSSs, our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS; the local upper bound (UB) on the filtering error covariance (FEC) is derived by solving the difference equations and minimized by designing proper filter gains. Furthermore, according to the local filters and their UBs, a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule. As such, the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers, thereby improving the estimation performance. Moreover, the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented. Finally, the validity of the developed algorithm is checked using a simulation example.

Cyber-physical systems (CPSs) take on the characteristics of both multiple rates of information collection and processing and the dependency on information exchanges. The purpose of this paper is to develop a joint recursive filtering scheme that estimates both unknown inputs and system states for multi-rate CPSs with unknown inputs. In cyberspace, the information transmission between the local joint filter and the sensors is governed by an adaptive event-triggered strategy. Furthermore, the desired parameters of joint filters are determined by a set of algebraic matrix equations in a recursive way, and a sufficient condition verifying the boundedness of filtering error covariance is found by resorting to some algebraic operation. A state fusion estimation scheme that uses local state estimation is proposed based on the covariance intersection (CI) based fusion conception. Lastly, an illustrative example demonstrates the effectiveness of the proposed adaptive event-triggered recursive filtering algorithm.

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches. The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network (NFN) and a linear state`-space model. The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals, which consist of step signals and random signals. First, based on the characteristic that step signals do not excite static nonlinear systems, that is, the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input, the unknown intermediate variables can be replaced by inputs, solving the problem of unmeasurable intermediate variable information. In the presence of step signals, the parameters of the state-space model are estimated using the recursive extended least squares (RELS) algorithm. Moreover, to effectively deal with the interference of measurement noises, a data filtering technique is introduced, and the filtering-based RELS is formulated for estimating the NFN by employing random signals. Finally, according to the structure of the Hammerstein system, the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system, and it can then be easily controlled by applying a linear controller. The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

This paper investigates the recoil control of the deepwater drilling riser system with nonlinear tension force and energy-bounded friction force under the circumstances of limited network resources and unreliable communication. Different from the existing linearization modeling method, a triangle-based polytope modeling method is applied to the nonlinear riser system. Based on the polytope model, to improve resource utilization and accommodate random data loss and communication delay, an asynchronous gain-scheduled control strategy under a hybrid event-triggered scheme is proposed. An asynchronous linear parameter-varying system that blends input delay and impulsive update equation is presented to model the nonlinear networked recoil control system, where the asynchronous deviation bounds of scheduling parameters are calculated. Resorting to the Lyapunov–Krasovskii functional method, some solvable conditions of disturbance attenuation analysis and recoil control design are derived such that the resulting networked system is exponentially mean-square stable with prescribed H_∞ performance. The obtained numerical results verified that the proposed nonlinear networked control method can achieve a better recoil response of the riser system with less transmission data compared with the linear control method.

Smart city situational awareness has recently emerged as a hot topic in research societies, industries, and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face. For example, in the latest five-year plan, the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things, for which situational awareness is normally the crucial first step. While traditional static surveillance data on cities have been available for decades, this review reports a type of relatively new yet highly important urban data source, i.e., the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the city. We especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System (GPS). This technique enjoys advantages such as a large penetration rate (~50% urban population covered), uniform spatiotemporal coverage, and high localization precision. We first discuss the pragmatic requirements for smart city situational awareness and the challenges faced. Then we introduce two suites of empowering technologies that help fulfill the requirements of (1) cybersecurity insurance for smart cities and (2) spatiotemporal modeling and visualization for situational awareness, both via big mobile data. The main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.

This paper presents a novel topology to control the baseband impedance of a power amplifier (PA) to avoid performance deterioration in concurrent dual-band mode. This topology can avoid pure resonance of capacitors and inductors LC, which leads to a high impedance at some frequency points. Consequently, it can be applied to transmitters that are excited by broadband signals. In particular, by adjusting the circuit parameters and increasing stages, the impedance of the key frequency bands can be flexibly controlled. A PA is designed to support this design idea. Its saturated output power is around 46.7 dBm, and the drain efficiency is >68.2% (1.8–2.3 GHz). Under concurrent two-tone excitation, the drain efficiency reaches around 40% even under 5.5 dB back-off power with the tone spacing from 10 MHz to 500 MHz. These results demonstrate that the proposed topology is capable of controlling wideband baseband impedance.

We demonstrate a low-noise, high-gain, and large-dynamic-range photodetector (PD) based on a junction field-effect transistor (JFET) and a charge amplifier for the measurement of quantum noise in Bell-state detection (BSD). Particular photodiode junction capacitance allows the silicon N-channel JFET 2sk152 to be matched to the noise requirement for charge amplifier A250. The electronic noise of the PD is effectively suppressed and the signal-to-noise ratio (SNR) is up to 15 dB at the analysis frequency of 2.75 MHz for a coherent laser power of 50.08 µW. By combining of the inductor and capacitance, the alternating current (AC) and direct current (DC) branches of the PD can operate linearly in a dynamic range from 25.06 µW to 17.50 mW. The PD can completely meet the requirements of SNR and dynamic range for BSD in quantum optics experiments.