This article focuses on the low-conservative stability criteria of delayed neural networks (DNNs). To achieve this goal, new techniques are developed to effectively utilize more system-related information. To use the time-varying delay information, some delay-product terms are introduced into the Lyapunov-Krasovskii functional (LKF), and an extended matrix-injection-based transformation method, which introduces delay-derivative-dependent slack matrices while obtaining the negative definite condition, is proposed. With respect to the use of activation function information, the terms related to the activation function are fully augmented in the LKF. In particular, by considering the sector-constraint information of the activation function, a new nonlinear-function-dependent functional term is established, and a sector-constraint-dependent matrix-separation-based inequality is developed. By applying the above techniques, several improved stability criteria are derived, and two typical examples are provided to illustrate the advantages of the proposed methods.

Load-frequency control (LFC) is commonly used in power systems to maintain the grid frequency. This article investigates the stability of LFC system containing time-varying delay with electric vehicles (EVs) and uncertain parameters. First, an improved model reconstruction technique is proposed to divide the state vectors of the system into$q$parts, where the first$q-1$parts are individual delay-state vectors and the$q{\text{th}}$part contains all other vectors. Unlike existing model reconstruction techniques that encapsulate all delay-state vectors into one part, the proposed method further reduces the dimensionality of the delay-state coefficient matrix. Second, based on the proposed model reconstruction technique, an augmented Lyapunov–Krasovskii functionals (LKF) is constructed in the form of additivity. This form reduces the number of decision variables, which greatly decreases computational complexity. Furthermore, in the derivative of LKF, the variable-augmented-based free-weighting-matrices technique is introduced only for time-delay state vectors, further reduces the conservatism with an acceptable computational complexity. Finally, two numerical examples are used and RT-LAB simulation experiment is conducted to verify that the proposed stability criterion can achieve high accuracy while reducing computational complexity significantly. Meanwhile, the effect of system parameters on delay margins is also discussed.

This paper investigates the stability analysis of the delayed T-S fuzzy systems with less conservatism by exploring the monotone increasing or decreasing characteristics of periodically varying delay. Firstly, we introduce a novel delay-set-partitioning-based (DSPB) approach via combining the allowable delay sets (ADS) method with the region partitioning methodology. Then, an improved DSPB Lyapunov–Krasovskii functional (LKF) together with DSPB FWMs method is proposed building upon this framework. Compared with the existing methods, the proposed methods tailor the construction of the LKF and FWM to each sub-ADS, thereby enhancing flexibility and reducing conservatism in stability analysis. As a result, we derive less conservative stability criteria for delayed fuzzy systems. Finally, the superiority and improvement of the proposed methods are validated through an illustrative numerical example.

This paper is concerned with the stability analysis of Takagi-Sugeno (T-S) fuzzy systems with time-varying delays. First, by constructing a generalized line-integral Lyapunov function (GLILF), applying the free-matrix-based inequality and the delay-derivative-dependent free-weighting-matrices method, a basic stability criterion is derived. Besides, the structure of the matrices in the stability criterion is reconstructed through congruent transformation, and an improved stability criterion is derived accordingly. To clarify the advancement of the proposed method, a stability condition is given without the GLILF. Finally, two numerical examples and a practical example are applied to show the advancement and merits of the presented method.

The frequency stability of multiarea power systems is guaranteed by networked load frequency control (LFC). Time delays due to occasional congestions/attacks in the LFC are often much longer than those from signal transmissions during normal communication, which invalidates the previous stability assessment methods. In this article, a novel stability analysis method for this scenario via a segmented delay description and a switched system is proposed. First, a two-piecewise function is used to describe multisource-induced delays, including large delays under occasional harsh network conditions and small delays under smooth network conditions; thus, a multiarea LFC model with multisource-induced delay is established. The stability criteria, which is based on switched system theory, reveals the relationship between delay characteristics and system stability. Finally, the proposed method is used to assess the delay tolerance of the LFC in traditional/deregulated environments. The results show that even with a large delay causing instability, as long as it meets certain frequency and duration constraints, the LFC remains stable. This novel discovery reflects the essential improvements of the proposed method and is useful for designing better control strategies.

This paper focuses on the stability analysis and stabilization design of Takagi-Sugeno (T-S) fuzzy systems under sampled-data control. Firstly, by capturing the constant characteristic of the sampling time in the sampling interval, a novel sampling-fuzzy-dependent LyapunovCKrasovskii functional (LKF) is proposed, which can introduce both fuzzy-dependent and sampling-dependent information and avoid the derivatives of membership functions (MFs). Secondly, a novel synchronization method via the slack membership-dependent matrices is proposed to synchronize the mismatched MFs between the plant and controller, which can overcome the limitations that the membership functions must be greater than zero in existing works. Thus, improved stability and stabilization conditions are obtained. Finally, two case studies are given to show the effectiveness and merits of the proposed methods.

This paper addresses the stability and stabilization issues of Takagi-Sugeno (T-S) fuzzy systems under sampled-data control. In this paper, efforts are dedicated to developing a stability criterion and control strategy with high accuracy and low complexity, leveraging an improved looped functional and a fuzzy-related matrix injection method. First, an improved looped function containing quadratic information about the sampling sawtooth characteristic is employed to include as much system state information as possible in a simple form. In response to the nonlinear terms encountered in the functional derivatives, a fuzzy-related matrix injection method is proposed on the basis of linearization transformation. This method enables the unified treatment of reciprocally convex and quadratic terms, which is conducive to reducing estimation gaps. Based on the aforementioned techniques, stability and stabilization criteria for T-S fuzzy systems are derived. The efficacy and superiority of the proposed method are substantiated through rigorous theoretical analysis and illustrative instances.

This article studies the stability and stabilization of delayed Takagi–Sugeno fuzzy systems (TSFSs). First, by introducing the membership functions into the free matrices, a membership-dependent (MD) Free-weighting-matrices (FWMs) method containing the variable-augmented-based FWMs method is proposed. This method can avoid the time-varying delay-dependent higher-order terms in the derivatives of the Lyapunov-Krasovskii functionals and introduce MD information. Then, an improved stability criterion is derived by applying the MD FWMs approach with the generalized free-matrix-based inequality. Moreover, through the parallel distributed compensation technology, a related controller design approach for the closed loop TSFS is developed. Finally, four known numerical examples are shown to validate the improvements and advancements of the presented method.

This article investigates the efficient model predictive load frequency control problem for multiarea power systems, where the measured state is transmitted under a new dynamic event-triggered mechanism (DETM) with hybrid variables. By applying$H_{2}$/$H_{\infty }$performance index, a DETM-based efficient model predictive control (EMPC) method is presented. This EMPC issue is described as a “min-max” optimization problem (OP) with hard constraints on system state. By utilizing a Lyapunov function with internal dynamic variable, an offline auxiliary OP with constraints of matrix inequalities is put forward to optimize the feedback gain and the weighting matrix of the DETM. Another offline OP is also proposed to maximize the size of initial feasible region (IFR). To steer the augmented state in IFR into the terminal constraint set and improve the control performance, a sequence of admissible control is implemented whose perturbation parameters are designed by solving an online OP. A case study on a three-area power system demonstrates the validity and superiority of the designed DETM and dynamic event-based EMPC algorithm.

This work focuses on the input-to-state stability (ISS) of digital filters with disturbances and time-varying delay in the presence of generalized overflow arithmetic. First, an extra-freedom-based lemma is derived to consider the information about the overflow arithmetic connecting the digital filter's state. Then, a novel delay-dependent ISS criterion of the digital filter is established based on the given lemma, which has less conservatism than the criterion described in the existing results. At last, the feasibility and advantages of the presented ISS analysis methods are demonstrated using a numerical simulation.

Sintering is the preproduction process of ironmaking, whose products are the basis of ironmaking. How to improve the operating performance of the iron ore sintering process has always been a problem that operators are committed to solve. An operating performance improvement method based on prediction and grade assessment is presented in this article. First, considering the data distribution characteristics of the process, a performance index prediction model based on the Gaussian process regression is built, in which the mutual information analysis method is used to select the inputs of the performance index prediction model. Then, the operating performance grade is assessed by a threshold division method. Next, the operating performance grade guides the control of the burn-through point to improve the operating performance. Finally, experimental verification is performed based on the actual running data. The results show that the proposed method has high prediction accuracy, and it is also significant in improving the operating performance. Therefore, this approach provides an effective solution to predict and improve operating performance.

This article presents a novel reconstructed model for the delayed load frequency control (LFC) schemes considering wind power, which aims to improve the computational efficiency for PID controllers while retaining their dynamic performance. Via fully exploiting system states influenced by time delays directly, this novel reconstructed method is proposed with a controller isolated. Hence, when the PID controllers are unknown, the stability criterion based on this model can resolve controller gains with less time consumed. For given PID gains, this model can be employed to establish criteria for stability analysis, which can realize the tradeoff between the calculation accuracy and efficiency. The case study is first based on a two-area traditional LFC system to validate the merits of a novel reconstructed model, including accurately estimating the influence of time delay on system frequency stability with increased computational capability. Then, under traditional and deregulated environments, case studies are carried out on the two-area and three-area schemes, respectively. Through the novel reconstructed model, the efficiency of obtaining controller parameters is highly improved while their robustness against the random wind power, tie-line power changes, inertial reductions, and time delays remains almost unchanged.

Uncertain transmission delays, sampling periods, parameters uncertainties regarding the power system, load fluctuations, and the intermittent generation of renewable energy sources (RESs) will significantly influence a power system's frequency. This article designs a robust delay-dependent PI-based load frequency control (LFC) scheme for a power system based on sampled-data control. First, a sampled-data-based delay-dependent LFC model of power system is constructed. Then, by applying the Lyapunov theory, and the linear matrix inequality technique, a novel stability criterion is developed for the LFC of the power system by considering the sampling period, and transmission delay of the communication network, which ensures that the proposed scheme operates in large sampling periods, and under transmission delays. Next, an exponential decay rate (EDR) is introduced to guide the design of a robust PI-based LFC scheme. The LFC scheme with robustness is designed by setting a small EDR. The values of EDR are adjusted by the given robust performance evaluation conditions of parameter uncertainties, and$H_\infty$performance. Finally, case studies are carried out based on a one-area power system, and a three-area power system with RESs. Simulation results show that the proposed LFC scheme performs strong robustness against parameter uncertainties regarding the power system, and communication network, load fluctuations, and the intermittent generation of RESs.