The secure consensus problem is addressed for multiagent systems (MASs) suffering from saturated control input and denial-of-service (DoS) attacks. The communication networks’ open setting and sharing nature give rise to security issues and impact the performance of MASs. Malicious DoS attacks attempt to disrupt the information exchange and undermine consensus by compromising the availability of transmitted data. Moreover, the control input can be saturated as a result of physical device limitations or safety concerns. To tackle these challenges, a state-prediction-based dynamic event-triggered mechanism (DETM) control protocol is designed to guarantee the secure consensus of MASs while reducing redundant communication, avoiding continuous monitoring of adjacent states, and ensuring effective utilization of limited bandwidth resources. Zeno behavior is eliminated by confirming the existence of a positive lower bound on interevent intervals. Sufficient conditions are established for the co-design of the DETM and controller to accomplish the desired goal. Finally, a simulation is conducted to substantiate the effectiveness and validity of the proposed control protocol.

The effective operation and management of battery-based energy storage devices hinge upon timely assessment of battery health. This paper introduces an optimized deep learning approach utilizing Artificial Neural Networks (ANN) for assessing the State of Health (SoH) of lithium-ion batteries, leveraging on battery aging datasets from NASA's Open Data Portal. An innovative methodology for optimizing Long Short-Term Memory (LSTM) hyperparameters using Genetic Algorithm (GA) is also proposed. Traditional approaches rely heavily on trial and error for hyperparameter determination, facing challenges in selecting optimal parameters due to an expansive search space. The proposed systematic methodology not only identifies the best architecture but also introduces novel criteria for evaluating ANN performance and complexity. The novel optimized GA-LSTM model is trained using the curated NASA data, and its accuracy evaluated by fine-tuning its parameters such as batch size, unit size, activation and loss functions. The results demonstrate that the proposed optimized GA-LSTM model exhibits better performance in estimating battery State of Health (SoH) across diverse conditions, achieving significantly improved Root Mean Square Error (RMSE) values compared to the Particle Swarm Optimization algorithm aided LSTM model, namely PA-LSTM, introduced in recent research. The improvement in RMSE ranges from 12.4% to 76.79% when trained with 70% of discharge cycle data. This represents a notable enhancement over previous methods, highlighting its efficacy as a more accurate prediction model for battery health assessment.

Finite-time observability of switching Boolean networks (SBNs) with Markov jump parameters (MJPs) is studied in this article. Via a parallel extension method, the observability of the considered SBN with MJPs is equivalent to that zero vector is reachable from an initial set of the new constructed system. A necessary and sufficient condition based on the extended structure matrix is presented for finite-time observability. Further, for unobservable systems, mode-dependent pinning control is first introduced and applied to achieve the observability. After the set of pinning subsystems is selected, for each pinning subsystem, mode-dependent pinning nodes, output-feedback controls (OFCs), and the adding approaches are designed. An algorithm is provided to find the set of pinning subsystems. Moreover, a necessary condition is given to solve mode-dependent pinning nodes, and the solvability of mode-dependent OFCs and the adding approaches are guaranteed. Finally, a numerical example is presented to show the effectiveness of the obtained results.

This paper explores the practical application and subsequent performance comparison of second-generation solar cells such as cadmium telluride (CdTe) and amorphous silicon (a-Si) photovoltaic (PV) cells to energize an ultra-low power wireless LoRa-based indoor internet of things (IoT) system. The significant advantages of the second-generation panels in terms of reduced capital cost, high-temperature performance, and good adaptation to low-light density compared to first-generation PV panels make them suitable for indoor IoT devices to provide better communication range and lifetime. A fully-functional CdTe PV-powered remote temperature and humidity station is designed and developed with a maximum effective communication range of 50 meters. The CdTe PV module used has an efficiency of 16%, which generates an output power of 3.$3mW$with an effective area of 215cm2under a 200 lux (1x) indoor environment. This module powers an ESP32-based LoRa node which operates in either normal or deep-sleep mode. The LoRa node consumes$165\mu W$and$260mW$of power while operating in deep-sleep mode and normal mode, respectively. With the implementation of a quiescent buck-boost converter and a supplementary energy storage system (ESS), the developed system can sustain the operation without any light at night. The developed prototype shows that compact and affordable PV cells are capable of providing continuous power to fulfill the demanding energy requirements of autonomous LoRa-based IoT systems, thus enabling fully autonomous operation for ultra-low power applications.

This study focused on the asynchronous event-triggered output-feedback controller design problem for discrete-time singular Markov jump systems (MJSs). A hidden Markov model (HMM) was employed to estimate the system mode, which cannot always be ideally detected in practice. Because the full state is also difficult to obtain in practical scenarios, an output-feedback control scheme was used. In addition, an HMM-based event-triggered mechanism was also employed in the design of the controller to reduce the communication burden of the networked system. Sufficient conditions for the stochastic admissibility of a closed-loop singular MJS with a prescribed$H_{\infty }$performance index were established using the Lyapunov functional technique. Finally, design procedures for an asynchronous event-triggered controller were summarized as a linear-matrix-inequality-based optimization algorithm. Two examples were considered to verify the effectiveness of the asynchronous event-triggered output-feedback controller design method.

Pinning control is applied to ensure the stabilization of Boolean networks (BNs) with time delay parameter. The time delay parameter in this article follows an independent identical distribution. Using semi-tensor product of matrices, the considered BN with time delay is converted to a high dimensional switching BN and the switching signal is the time delay signal. Different from general switching BN, the structure matrices of all subsystems are independent with each other, structure matrices of all subsystems for the high dimensional switching BN depend on the original BN structure matrix. Pinning control is designed to guarantee the global stochastic stability of the considered system. Furthermore, the global stochastic stability of BN with time delay parameter is proved to be equivalent to the stability of BN without time delay parameter, which greatly reduces the computational complexity and simplifies the control design. For the considered BN with finite cycles, constrained pinning control is applied with limitation that only one state of each undesired cycle is under control. With this constraint, the minimal number of pinning control nodes is further investigated. Some algorithms are presented to obtain the new structure matrix, with which, pinning control can be solved by the obtained methods. Both numerical examples and biological example illustrate the effectiveness of the results.

This article focuses on the secure consensus problem of linear multiagent systems (MASs) with quantized event-triggered control (ETC) against denial-of-service (DoS) attacks. DoS attacks with constrains on frequency and duration are discussed, which intend to block the communication links between agents to destroy the consensus. Due to the limited transmission capacity and communication resources, a uniform quantizer and an event-triggered mechanism (ETM) are considered to economize energy consumption. An ETC protocol based on the quantized relative state is designed to resist malicious DoS attacks. Then, sufficient conditions to guarantee the practical consensus of MASs are derived with finite data transmission rates, and the tolerance of DoS attack frequency and duration are given. The Zeno behavior does not exhibit by proving that lower positive bounds exist for all agents, indicating the feasibility of the proposed ETM. Finally, two simulation examples are given to verify the validity and superiority of the theoretical results.

This paper investigates the leader-following consensus control problem for a class of nonlinear multiagent systems subject to sensor and actuator faults under a fixed directed graph. First, a fault compensation mechanism is proposed because of multiple faults wherein the adaptive parameters substitute the fault coefficients. Then, the command filtering method is employed to avoid the burst of complexity rendered by the duplicative differentiation of the virtual control signal. Furthermore, the neural networks-based state observers are designed to reconstruct the unmeasurable states of the nonlinear multiagent systems. According to the given design approach, a switching threshold-based event-triggered adaptive fault-tolerant control strategy is developed and ensures all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, the simulation result is provided to demonstrate the validity of the presented method.