Publications

In this article, the ultrafast tracking control for high-order discrete-time multiagent networks is investigated based on the predictive ability of agents. First, in view of the evolution of the original network dynamics, a distributed multistep prediction algorithm is established. Further, a control strategy containing predictive information is presented to achieve the ultrafast tracking control. Second, the problem of ultrafast tracking control with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance specification is dealt with. The obtained result indicates that the robustness of the whole multiagent network can be converted into the robustness of multiple separable subsystems. Finally, the optimal ultrafast static tracking control design is provided for the single-integrator network with sampled dynamics. Two simulation cases are carried out to verify the correctness of the obtained theoretical results.

Online social media platforms have become a crucial part of teachers' professional life. They utilize these platforms for various purposes, e.g., creating professional communities, curating pedagogical resources, and interacting with their students. Hence, recent years have witnessed many studies attempting to inform us about opportunities and challenges of social media usage by PK-12 teachers. Nevertheless, how male and female teachers leverage online social media, particularly P interest, is still being determined. Given the importance of online social media in shaping teaching careers and that teachers' gender arguably affects educational achievements and learning environments, it is essential to delineate teachers' online (professional) behavior on social media while considering their genders. To this end, first, we build a large dataset of teachers on P interest with fine-grained information about their online activities. T hen, we perform a thorough data analysis of the online behavior of male and female teachers. In particular, we shed light on similarities and differences in the resource curation process of both gender groups, e.g., the topics and sources of their resources. The novel findings of this paper serve as a valuable reference for many entities concerned with gender-aware teaching practices, such as educational scholars, policymakers, and state-level and national-level government agencies.

This chapter examines the concept of the leader–follower formation for fractional-order multi-agent systems (FO-MASs) by utilizing dynamic event-triggered sliding mode control (SMC) method. First, a dynamic event-triggered mechanism (DETM) is designed relying on the combined measurement vector, in which an auxiliary variable is introduced to dynamically adjust the threshold for each fractional-order agent system. Unlike most existing event-triggered communication mechanisms, whose threshold parameters are always fixed, the threshold parameters in our designed event-triggered conditions can be dynamically adjusted according to the fractional-order dynamic rule. Numerical results show that the proposed DETM can achieve a better system performance in reducing the sampling frequency and the expected formation performance. Second, the leader–follower formation control problem is transformed into checking the asymptotic stability problem of the closed-loop system. In addition, due to the memorability of fractional calculus operators, a distinctive condition is established to avoid the occurrence of Zeno behavior reported in existing dynamic event-triggered schemes. Finally, a simulation example is presented to illustrate the effectiveness and feasibility of the dynamic event-triggered SMC method proposed in this chapter.

In this paper, the measurement outlier-resistant target tracking problem is investigated in wireless sensor networks (WSNs) with energy harvesting constraints. Each WSN node can acquire energy stochastically from surroundings. No matter whether the WSN node acquires energy or not, the WSN node’s measurement can be transmitted if the energy amount of the WSN node is greater than zero. In such a case, the sensor energy-induced missing measurement (SE-IMM) phenomenon may occur. The objective of this paper is to develop a solution for the considered target tracking problem by devising the filter including a saturation constraint such that, in the simultaneous presence of outliers and the SE-IMM phenomenon, the tracking performance can meet the given performance index. Firstly, the relation between the energy level of the WSN node and its probability distribution is computed recursively. Then, an upper bound of the tracking error covariance is derived which is minimized by appropriately choosing the filter parameter. Finally, the feasibility of the proposed target tracking scheme is validated by conducting a set of comparative experiments and the relationship between the energy of the WSN node and the tracking performance is also disclosed.

No abstract is provided for this article.

In this paper, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is presented. By using a radial basis (RBF) neural network, a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. The state-feedback is based on Lyapunov stability theory, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inputs. Simulation results on dynamic equations of vertical take-off and landing (VTOL) helicopter confirm the effectiveness of the proposed methods in the stabilization of mismatched nonlinear systems.

This paper is concerned with the problem of sampled-data exponential synchronization for chaotic Lur'e systems (CLSs) in the form of master-slave framework. An improved time-dependent Lyapunov functional (TDLF) is put forward to fully exploit the accessible information about sampling characteristics and nonlinearities of the CLS. By resorting to the improved TDLF, a new synchronization criterion is established, which ensures the synchronization error system is globally exponentially stable. An illustrative example is offered to demonstrate the validity and virtue of the proposed design methodology.

This paper is concerned with finite-time sliding mode control (SMC) of continuous-time semi-Markovian jump systems with immeasurable premise variables via fuzzy approach. First, an integral sliding surface is constructed based on fuzzy observer. Second, an observer-based SMC law is synthesized to guarantee finite-time reachability of the predefined sliding surface before the prescribed time. Third, through finite-time boundedness analysis, the required boundedness performance is conducted at the reaching phase first and then the sliding motion phase, respectively. Furthermore, sufficient conditions in terms of linear matrix inequalities (LMIs) are established to guarantee the required boundedness performance of the overall closed-loop controlled system during the two phases with generally uncertain transition rates (TRs) simultaneously. Finally, a practical example is given to show the validity of the established method numerically.

Intelligent fault diagnosis is a rapidly evolving field within power engineering. Using gas-in-oil data is a reliable method for transformer fault diagnosis that has been widely adopted in the power industry. However, traditional machine learning methods often suffer from low diagnostic accuracy due to the lack of a clear and effective feature set for gas-in-oil data as well as an imbalance between classes of sample size. To overcome this challenge, this paper proposes a novel transformer fault diagnosis model that utilizes a Filter-Wrapper Combined Feature Selection method and an AdaBoost integrated weighted broad learning system (AdaBoost-WBLS). More specifically, the original data is expanded to extract meaningful features, and the Filter-Wrapper combined feature selection method is used to eliminate preliminary redundancy, relevance, and significance of current features. The Wrapper algorithm is then used for precise screening to obtain the optimal feature subset, which effectively improves the quality of transformer features. Furthermore, to address the issue of imbalanced transformer samples, an improved BLS and AdaBoost integration method is introduced, and a fault diagnosis model based on AdaBoost-WBLS is proposed. Compared with existing power transformer fault diagnosis methods, the proposed method has a more accurate and balanced effect on fault classification. Overall, this paper provides a comprehensive and effective approach to transformer fault diagnosis, which has important implications for the reliability and safety of power systems.

We focus on the issue of robust stabilization with H ∞ performance for a class of linear time‐invariant parameter‐dependent systems under norm‐bounded nonlinear uncertainties. By combining the idea of polynomially parameter‐dependent quadratic Lyapunov functions and linear matrix inequalities formulations, some parameter‐independent conditions with high precision are given to guarantee robust asymptotic stability and robust disturbance attenuation of the linear time‐invariant parameter‐dependent system in the presence of norm‐bounded nonlinear uncertainties. The parameter‐dependent state‐feedback control is designed based on the Hamilton‐Jacobi‐Isaac (HJI) method. The applicability of the proposed design method is illustrated in a simple example.

This article discusses the issue of input-output finite-time generalized dissipative filter design for a class of discrete time-varying systems. First, an adaptive event-triggered mechanism (AETM) with an adaptive law is proposed to adjust the threshold in the AETM according to the error between the system states and the filter states. Such an AETM determines whether the measurement output should be transmitted or not, which is more effective to economize the communication resources comparing with the traditional event-triggered mechanism. Second, in view of network-induced delays, the quantization and the AETM, a time-varying filter error system (TV-FES) is modeled. Then, a new augmented time-varying Lyapunov functional containing triple sum terms is provided. Based on the new finite-sum inequality and improved reciprocally convex combination lemma, delay-dependent conditions are obtained, which can ensure the TV-FES to be input-output finite-time stable and satisfy the given generalized dissipative performance. Moreover, the recursive linear matrix inequalities are presented to obtain the desired filter gains. Finally, numerical examples demonstrate the superiority and feasibility of the proposed method in this article.

This paper investigates the stability and asynchronous L 1 control problems for a class of switched positive linear systems (SPLSs) with time-varying delays by using the mode-dependent average dwell time (MDADT) approach. By allowing the co-positive type Lyapunov–Krasovskii functional to increase during the running time of active subsystems, a new stability criterion for the underlying system with MDADT is first derived. Then, the obtained results are extended to study the issue of asynchronous L 1 control, where “asynchronous” means that the switching of the controllers has a lag with respect to that of system modes. Sufficient conditions are provided to guarantee that the resulting closed-loop system is exponentially stable and has an L 1-gain performance. Finally, two numerical examples are given to show the effectiveness of the developed results.

No abstract is provided for this article.

No abstract is provided for this article.

In this paper, a computationally effective strategy to obtain multioverlapping controllers via the Inclusion Principle is applied to design discrete‐time state‐feedback multioverlapping LQR controllers for seismic protection of tall buildings. To compute the corresponding control actions, the proposed semidecentralized controllers only require state information from neighboring stories. This particular configuration of information exchange allows introducing a dramatic reduction in the transmission range required for a wireless implementation of the communication system. To investigate the behavior of the proposed semidecentralized multioverlapping controllers, a proper simulation model has been designed. This model includes semiactive actuation devices with limited force capacity, control sampling times consistent with the communication latency, time‐delayed state information, and communication failures. The performance of the proposed multioverlapping controllers has been assessed through numerical simulations of the seismic response of a 20‐story building with positive results.

Qanat is considered as an ancient and sustainable water management system that does exist in many parts of the world, especially in Middle East and Iran. The main goal of this chapter is to introduce Qanat irrigations system. To this end, two techno-physical and social-cultural sub-systems of Qanats were clearly explained. In general, in terms of techno-physical system, Qanats can be considered as gently sloping subterranean tunnels that link wells over long distances. In other words, the techno-physical system of the Qanat consists of a series of vertical shafts or chains of wells that are connected using steep horizontal tunnels. On other hand, social-cultural system of Qanat irrigation system typically deals with this issue that Qanat is a substantial social phenomenon and should not be viewed only as an engineering wonder. In other words, Qanat system has to be closely linked to the local communities' capacity to plan and manage their own water resources, especially for agriculture. The social institution on which Qanat depends to operate properly is called Buneh. Buneh is defined as a social system in Qanat regions and has been the product of adaptive reaction of human to water shortages in which individuals who are subject of the water scarcity share the managerial and ownership activities of water resources using a complex sharing ethics among local stakeholders. In addition, based on the literature review, some of the contributions of Qanats irrigation system to the sustainability of water resources are introduced. In conclusion, despite centuries of use, Qanats are still important in some countries, since they require little technology to operate and are considered as a sustainable system of water resource utilization, especially in low-water areas.

This paper deals with a robust<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>deconvolution filtering problem for discrete-time nonlinear stochastic systems with randomly occurring sensor delays. The delayed measurements are assumed to occur in a random way characterized by a random variable sequence following the Bernoulli distribution with time-varying probability. The purpose is to design an<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>deconvolution filter such that, for all the admissible randomly occurring sensor delays, nonlinear disturbances, and external noises, the input signal distorted by the transmission channel could be recovered to a specified extent. By utilizing the constructed Lyapunov functional relying on the time-varying probability parameters, the desired sufficient criteria are derived. The proposed<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>deconvolution filter parameters include not only the fixed gains obtained by solving a convex optimization problem but also the online measurable time-varying probability. When the time-varying sensor delays occur randomly with a time-varying probability sequence, the proposed gain-scheduled filtering algorithm is very effective. The obtained design algorithm is finally verified in the light of simulation examples.