The objective of this brief is to focus on the problem of output tracking control for a class of fractional-order positive switched systems via an observer-based controller method that combines equivalent-input-disturbance approach and Smith predictor. By employing Lyapunov theory together with average dwell-time approach, a new exponential stability criterion is derived in terms of linear matrix inequalities for the resulting closed-loop system. Based on the derived delay-dependent criterion, a design method of the proposed controller is then presented. The designed controller can assure that the output signals of the system trace the specified reference signals within the preferred neighborhood of the equilibrium. Furthermore, the solvability inclusive conditions for the proposed controller design of the considered system are established according to the state being available or not. Numerical simulation results are provided to demonstrate the strong disturbance rejection capability and the superiority of the proposed control design method over some existing ones.
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.
In the car industry, the Finite Element Method (FEM) is being more and more used to analyze the crashworthiness performance of vehicles. In order to validate the results, these impact simulations are normally compared with real crash footage and acceleration data. This paper studies the deformation- and energy output of a simple dummy model during a non-linear dynamic impact. The dummy model is crashed into an obstacle at three different velocities to observe the energy dissipated through different damping mechanisms. Furthermore, in impact simulations, material damping plays an important role in energy dissipation. However, it can be difficult to determine realistic damping parameter values, and they almost certainly need to be verified through experiments. For further work, these results will be used to validate and calibrate an impact simulation of a dummy model attached to a notch impact equipment.
Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.
No abstract is provided for this article.
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.
Adaptive fuzzy sliding mode controller for a class of uncertain nonlinear systems is proposed in this paper. The unknown system dynamics and upper bounds of the minimum approximation errors are adaptively updated with stabilizing adaptive laws. The closed-loop system driven by the proposed controllers is shown to be stable with all the adaptation parameters being bounded. The performance and stability of the proposed control system are achieved analytically using the Lyapunov stability theory. Simulations show that the proposed controller performs well and exhibits good performance.
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.
Quality-related fault detection and diagnosis (QrFDD) is an emerging research subject in the field of multivariate statistical process monitoring and has received great attention from academia and industry in recent years. Compared with traditional multivariate statistical process monitoring methods, QrFDD methods can decompose the process variable space into orthogonal subspaces according to the correlation between input and output so that faults affecting output and faults that do not affect output can be diagnosed in different subspaces. Thanks to this feature, the QrFDD methods have important application values in reducing unnecessary maintenance time and costs, as well as improving production efficiency. Since the decade so far proposed, many outstanding research results have been produced; however, the technical route and implementation algorithm of these achievements are not all the same. In this chapter, we will conduct a technical review and summary of the classical achievements, including their principles, implementation algorithms, technical advantages, and defects. At the same time, we will introduce some of our latest research results and look forward to the future development trend of QrFDD from the perspectives of technology and demand.
This paper is concerned with the problem of sampled-data piecewise-affine (PWA) filter design of PWA systems via continuous piecewise Lyapunov functionals. Especially, an input delay scheme is initially introduced to characterize the sample-and-hold behavior with a time-varying delayed measurement output. Then, an improved ℋ ∞ performance analysis criterion is presented for the filtering error systems, which is achieved by constructing a novel continuous piecewise Lyapunov–Krasovskii functional and an extended integral inequality. Furthermore, with a linearization procedure, the full-order and reduced-order PWA filter synthesis is developed in a unified framework. Simulation studies are conducted to demonstrate the efficacy and less conservativeness of the proposed approach.
This note deals with the control of uncertain highly nonlinear biological processes. Indeed, an adaptive fuzzy control (AFC) scheme is developed for the pre-treatment of wastewater represented by a Takagi–Sugeno (TS) fuzzy model. The proposed approach uses a fuzzy system to approximate the unknown substrate consumption rate in designing the adaptive controller, and then an observer is designed to estimate the concentration in substrate at the outlet bioreactor. The observer is employed to generate an error signal for the adaptive control law which permits to minimize the influence of the measurement noise on the estimation of the substrate concentration. An update of the fuzzy models parameters are obtained using Lyapunov׳s second method. The closed-loop system behavior is then illustrated on a noisy simulation.
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.
In this paper, we present an advanced computational procedure that allows obtaining distributed energy-dissipation systems for large multi-story structures. The proposed methodology is based on a decentralized velocity-feedback energy-to-componentwise-peak (ECWP) controller design approach and can be formulated as a linear matrix inequality (LMI) optimization problem with structure constraints. To demonstrate the effectiveness of the proposed design methodology, a passive damping system is computed for the seismic protection of a 20-story building equipped with a complete set of interstory viscous dampers. The high-performance characteristics of the obtained passive ECWP control system are clearly evidenced by the numerical simulation results. Also, the computational effectiveness of the proposed design procedure is confirmed by the low computation time of the associated LMI optimization problem.
