Nonfragile power and frequency control in islanded microgrids under abnormal asynchronous stochastic cyber attacks.

In this paper, we focus on the real power sharing and frequency regulation of distributed generators in islanded microgrids with abnormal asynchronous stochastic cyber attacks, which is of great significance to the information security and stable operation of microgrids. Firstly, considering the possible cyber attacks in the communication network, a distributed non-fragile controller with coupled memory delay is proposed according to the nonperiodic sampled-data control. Then, the construction of delay-dependent two-sided looped-functional makes the Lyapunov-Krasovskii functional contain more delay and sampling information and relaxes constraints on free matrices. In addition, based on the enhanced integral inequality technique and the linear convex combination method, a sampling-based consensus protocol is presented to solve the issues of real power sharing and frequency regulation in the islanded microgrids. Finally, to verify the effectiveness and feasibility of the designed control strategy, a modified IEEE 34-bus test system is used for the experiment.

Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights.

This paper addresses fixed-time output synchronization problems for two types of complex dynamical networks with multi-weights (CDNMWs) by using two types of adaptive control methods. Firstly, complex dynamical networks with multiple state and output couplings are respectively presented. Secondly, several fixed-time output synchronization criteria for these two networks are formulated based on Lyapunov functional and inequality techniques. Thirdly, by employing two types of adaptive control methods, fixed-time output synchronization issues of these two networks are dealt with. At last, the analytical results are verified by two numerical simulations.

Dynamic-Memory Event-Triggered H∞ Load Frequency Control for Reconstructed Switched Model of Power Systems Under Hybrid Attacks.

In this work, a novel dynamic-memory event-triggered H∞ load frequency control (LFC) approach for the power system is proposed considering the existence of hybrid attacks. A dynamic-memory event-triggered mechanism (DMETM) is first presented under denial-of-service (DoS) attacks to reduce the occupation of network communication bandwidth. Different from the existing event-triggered mechanisms (ETMs), the superiority of DMETM is that not only the past transmitted packets can be utilized but also the amount of utilized packets can be adjusted according to the state error of the power system. Then, the general LFC model of the power system is reconstructed as a switched system on account of the existence of DoS attacks and deception attacks. Based on the reconstructed switched model, an exponentially mean-square stability criterion with an H∞ performance index is derived by constructing appropriate Lyapunov-Krasovskii functionals (LKFs). Furthermore, the DMETM controllers and event-triggered weighting matrices can be obtained by solving the relevant linear matrix inequalities (LMIs). Finally, some illustrated examples are presented to demonstrate the feasibility and effectiveness of the approach proposed.

A Novel Reconstruction Method for Temperature Distribution Measurement Based on Ultrasonic Tomography.

The precise temperature distribution measurement is crucial in many industrial fields, where ultrasonic tomography (UT) has broad application prospects and significance. In order to improve the resolution of reconstructed temperature distribution images and maintain high accuracy, a novel two-step reconstruction method is proposed in this article. First, the problem of solving the temperature distribution is converted to an optimization problem and then solved by an improved version of the equilibrium optimizer (IEO), in which a new nonlinear time strategy and novel population update rules are deployed. Then, based on the low-resolution and high-precision images reconstructed by IEO, Gaussian process regression (GPR) is adopted to enhance image resolution and keep the reconstruction errors low. After that, the number of divided grids and the parameters of IEO are also further studied to improve the reconstruction quality. The results of numerical simulations and experiments indicate that high-resolution images with low reconstruction errors can be reconstructed effectively by the proposed IEO-GPR method, and it also shows excellent robust performance. For a complex three-peak temperature distribution, a competitive accuracy with 3.10% and 2.37% error at root-mean-square error and average relative error is achieved, respectively. In practical experiment, the root-mean-square error of IEO-GPR is 0.72%, which is at least 0.89% lower than that of conventional algorithms.

Secure consensus of multi-agent systems with redundant signal and communication interference via distributed dynamic event-triggered control.

This paper studies a class of multi-agent systems (MASs) subject to deception signal and communication interference. The objective of the present work is to establish a flexible and generalized distributed dynamic event-triggered control (DDETC) with impulsive signal to make the investigated MASs achieve secure consensus under redundant signal and communication interference. It is shown that Zeno behavior can be precluded with such a DDETC. The challenging but valuable new designed DDETC scheme shows the trigger is developed to achieve itself away from exceeding the data transmission load through parameter adjustment, to reduce redundant triggering, to flexibly adjust the triggered frequency, and even to replace sampled-data scheme as special cases. By the impulsive DDETC, anti-deception and anti-interference techniques, the secure consensus criteria of MASs are constructed cleverly. Numerical examples with simulations are given to illustrate the effectiveness of the proposed scheme and control protocol.

Improved results on state feedback stabilization for a networked control system with additive time-varying delay components' controller.

This paper investigates the problems of stability and stabilization for a networked control system (NCS) with additive time-varying delay components' controller. Firstly, stability of a NCS with additive time-varying delays is investigated. A novel approach with free parameters is proposed. By constructing a new Lyapunov-Krasovskii functional (LKF) with two free parameters, stability criteria are obtained. The obtained stability criteria depend not only on upper bounds of delays but also free parameters. In addition, input-output method is extended to study the stability problem for the NCS. Compared with other approaches such as input-output method, the free-parameter approach is more flexible and effective in reducing the conservatism. Then, based on the stability results, a state feedback controller is designed to guarantee the asymptotically stable of the closed-loop systems. Finally, numerical examples are provided to show the effectiveness and less conservatism of the proposed results.

State of charge estimation of lithium-ion batteries using fractional order sliding mode observer.

This paper presents a state of charge (SOC) estimation method based on fractional order sliding mode observer (SMO) for lithium-ion batteries. A fractional order RC equivalent circuit model (FORCECM) is firstly constructed to describe the charging and discharging dynamic characteristics of the battery. Then, based on the differential equations of the FORCECM, fractional order SMOs for SOC, polarization voltage and terminal voltage estimation are designed. After that, convergence of the proposed observers is analyzed by Lyapunov's stability theory method. The framework of the designed observer system is simple and easy to implement. The SMOs can overcome the uncertainties of parameters, modeling and measurement errors, and present good robustness. Simulation results show that the presented estimation method is effective, and the designed observers have good performance.

Delay-dependent finite-time boundedness of a class of Markovian switching neural networks with time-varying delays.

In this paper, a novel method is developed for delay-dependent finite-time boundedness of a class of Markovian switching neural networks with time-varying delays. New sufficient condition for stochastic boundness of Markovian jumping neural networks is presented and proved by an newly augmented stochastic Lyapunov-Krasovskii functional and novel activation function conditions, the state trajectory remains in a bounded region of the state space over a given finite-time interval. Finally, a numerical example is given to illustrate the efficiency and less conservative of the proposed method.

Finite-time boundedness filtering for discrete-time Markovian jump system subject to partly unknown transition probabilities.

This paper investigates the problem of finite-time boundedness filtering for discrete-time Markovian jump system subject partly unknown transition probabilities. By using the multiple Lyapunov function approach, a novel sufficient condition for finite-time bounded of H∞ filtering is derived and the system trajectory stays within a prescribed bound during a specified time interval. Finally, an example is provided to illustrate the usefulness and effectiveness of the proposed method.