1,256 publications from this institution
This paper focuses on the stability and stabilization problem for a neutral impulsive switching system with time-varying delay. Based on LMI method and optimization technologies, some stability criteria are derived for this kind of system. Some example and numerical simulation are given to demonstrate the effectiveness of our theoretical results.
Summary This paper addresses the problem of a hierarchical sliding mode surface (HSMS) control design for nonlinear systems via a dynamic event‐triggered mechanism. Initially, the HSMS containing the system states is constructed to enhance the system's response rate and robustness. By assigning a cost function associated with the HSMS, such an control problem is equivalently transformed into a zero‐sum game problem, where the control policy and the exogenous disturbance are treated as two players with opposite interests. Afterwards, a novel dynamic event‐triggered mechanism is designed, where the triggering condition depends on HSMS variables. To solve the corresponding event‐triggered Hamilton–Jacobi–Isaacs equation, a single‐critic reinforcement learning algorithm is developed, which removes the error generated by approximating the actor network in the actor‐critic network. According to the Lyapunov stability theory, all signals of the considered system are strictly proved to be bounded. Finally, the validity of the proposed control method is demonstrated through simulations of a tunnel diode circuit system and a mass‐spring‐damper system.
One of the primary issues of many websites is the suggestion of multiple choices to the users at the same time, which makes the task more complex and time consuming to find the right product. Web mining and recommendation system based on user behaviour helps users by providing essential information without asking explicitly. Several movie recommendation systems are available to suggest movies, but often they do not do that effectively. To achieve enhanced effectiveness and efficiency, user's movie ratings were retrieved, cleaned, formatted and grouped into proper, meaningful session and data profile was developed. In this paper, we have developed a new ontology for clear and better understanding of the movie domain. The user data consisting of movie ratings is used to recommend movies for the users. For the classification of users, we use adaptive K-nearest neighbour (AKNN) approach and post classification process; movies are recommended to the active target user. The obtained results of the proposed recommendation approach are compared with existing baseline methods, and the results prove that the presented approach to be proficient.
This paper focuses on the development of mathematical models for vehicle frontal crashes. The models under consideration are threefold: a vehicle into barrier, vehicle-occupant and vehicle to vehicle frontal crashes. The first model is represented as a simple spring-mass-damper and the second case consists of a double-spring-mass-damper system, whereby the front mass and the rear mass represent the vehicle chassis and the occupant, respectively. The third model consists of a collision of two vehicles represented by two masses moving in opposite directions. The springs and dampers in the models are nonlinear piecewise functions of displacements and velocities respectively. More specifically, a genetic algorithm (GA) approach is proposed for estimating the parameters of vehicles front structure and restraint system for vehicle-occupant model. Finally, using the existing test-data, it is shown that the obtained models can accurately reproduce the real crash test data.
This paper is concerned with the stabilization problem of discrete-time systems with stochastic sampling. It is assumed that there are a single-rate sampling in the plant input and two stochastic sampling rates in the controller input whose occurrence probabilities are given constants and satisfy a Bernoulli distribution. By Lyapunov function approach, a new sufficient condition is presented for the mean square asymptotic stability of the system. Based on this, the design procedure for stabilization controllers is proposed. Finally, an example is given to demonstrate the effectiveness of the proposed techniques.
This paper addresses the problem of state and fault estimation for linear continuous-time systems with actuator failures. A new estimation technique is presented to deal with this design problem. In the proposed approaches, the original system is first augmented in a descriptor system, and a new sliding mode observer is introduced to obtain accurate estimations of both system states and actuator faults. A numerical example is presented to illustrate the effectiveness and applicability of the proposed technique.
In this paper, the sliding mode control (SMC) design for nonlinear stochastic semi-Markov switching systems (S-MSSs) is studied via the bound of time-varying transition rate matrix, in which semi-Markov switching parameters, stochastic disturbance, uncertainty, and nonlinearity are all considered in a unified framework. The system under consideration is more general, which covers the Markov switching system with sojourn-time-independent transition rate matrix as a special case. Many practical systems subject to unpredictable structural variations can be characterized by nonlinear stochastic S-MSSs with sojourn-time-dependent transition rate matrix. The specific information about the bound of time-varying transition rate matrix is known for the sliding mode controller design. First, by using the stochastic semi-Markov Lyapunov function, sojourn-time-dependent sufficient conditions are developed to guarantee the closed-loop sliding mode dynamics stochastically stable. Then, the SMC law is constructed to ensure the reachability of the sliding mode dynamics in a finite-time level. Finally, one joint of space robot manipulator model is described as nonlinear stochastic S-MSSs to illustrate the validity of the proposed SMC design method.
With the emergence of online social media platforms, there has been a surge of teachers/educators turning to these platforms for professional purposes, e.g., supplementing their students' educational needs. Consequently, teachers in social media have been the subject of many educational studies. Despite the progress in this line of research, one of the major obstacles is the limited number of teachers being investigated. Current studies usually suffice to at most a few hundreds of surveyed teachers while there are thousands of other teachers online. To better understand teachers in online social media and enable modern machine learning approaches to process teacher-related data, we need to identify more teachers. Thus, this paper proposes a framework to automatically identify teachers on Pinterest– an image-based social media platform popular among teachers. We formulate the teacher identification problem as a positive unlabeled learning task where positive samples are a small set of surveyed teachers, and unlabeled samples are their connected users on Pinterest. We perform extensive experiments on a real dataset of teachers on Pinterest and show the effectiveness of our framework. We believe the proposed framework can potentially improve the quality of many research endeavors concerned with studying teachers in social media.
1Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China 2Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43202, USA 3Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway 4School of Automation, Harbin Engineering University, Harbin 150001, China 5Deep Space Exploration Research Center, School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
In this paper mathematical modelling of a vehicle crash based on a lumped parameter model is studied. The vehicle is modelled as a single mass connected to a non-linear spring and damper system. The characteristics of the non-linear behaviour of the model is identified with a hybrid Firefly/Harmony Search optimization algorithm that minimizes the deviation between experimental test data and a simulated response. The experimental data is taken from three crashes of an identical vehicle that crashes into a wall at different initial velocities. The aim of this paper is to find a piecewise-linear function for the spring and damper coefficients which is scaleable to reconstruct the three different experimental crashes at different impact velocities. Numerical results are provided to illustrate the applicability of the proposed algorithm. Three data sets will be used for parameter identification and a fourth data set will be used for verification
This paper deals with the design of robust fault detection system for Takagi-Sugeno (T-S) modes with parametric uncertainties and time-varying delay. An Unknown Input Observer (UIO) is designed such that the unknown inputs are thoroughly decoupled from residual signals while they show the maximum possible sensitivity to the faults and the minimum possible sensitivity to the external disturbances. Since the system under consideration is subjected to parametric uncertainties, the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> model matching approach is used to design an optimal observer. Design procedure is given in terms of Linear Matrix Inequalities (LMIs). Finally, a numerical example is presented to show the effectiveness of the proposed method.
In vibration control of compound structures, inter-substructure damper (ISSD) systems exploit the out-of-phase response of different substructures to dissipate the kinetic vibrational energy by means of inter-substructure damping links. For seismic protection of multistory buildings, distributed sets of interstory fluid viscous dampers (FVDs) are ISSD systems of particular interest. The connections between distributed FVD systems and decentralized static output-feedback control allow using advanced controller-design methodologies to obtain passive ISSD systems with high-performance characteristics. A major issue of that approach is the computational difficulties associated to the numerical solution of optimization problems with structured bilinear matrix inequality constraints. In this work, we present a novel iterative linear matrix inequality procedure that can be applied to obtain enhanced suboptimal solutions for that kind of optimization problems. To demonstrate the effectiveness of the proposed methodology, we design a system of supplementary interstory FVDs for the seismic protection of a five-story building by synthesizing a decentralized static velocity-feedback H∞ controller. In the performance assessment, we compare the frequency-domain and time-domain responses of the designed FVD system with the behavior of the optimal static state-feedback H∞ controller. The obtained results indicate that the proposed approach allows designing passive ISSD systems that are capable to match the level of performance attained by optimal state-feedback active controllers.
The H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> synchronization problem of the master and slave structure of a second-order neutral master-slave systems with time-varying delays is presented in this paper. Delay-dependent sufficient conditions for the design of a delayed output-feedback control are given by Lyapunov-Krasovskii method in terms of a linear matrix inequality (LMI). A controller, which guarantees H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> synchronization of the master and slave structure using some free weighting matrices, is then developed. A numerical example has been given to show the effectiveness of the method.
Recently several studies have explored the realization of autonomous control in production and logistic operations. In doing so, it has been tried to transmit the merit of decision-making from central controllers with offline decisions to decentralized controllers with local and real-time decision makings. However, this mission has still some drawbacks in practice. Lack of global optimization is one of them, i.e., the lost chain between the autonomous decentralized decisions at operational level and the centralized mathematical optimization with offline manner at tactical and strategic levels. This distinction can be reasonably solved by considering fuzzy parameters in mathematical programming to meet the required tolerances for autonomous objects at operational level. This claim is recommended and partially experimented in this paper. An assembly scenario is modeled by a discrete-event simulation, in which autonomous pallets carry products throughout the system. This scenario is optimized with regard to its objectives in a simulation, while fuzzy parameters in optimization programming can consider autonomous decisions done at operational level.
No abstract is provided for this article.
The problem of sliding mode control (SMC) for a class of Markov jump systems (MJSs) is addressed in this paper based on a resource-aware triggering mechanism which realizes computational resources saving and disturbance attenuation simultaneously. By introducing the self-triggered policy, the next execution time is pre-computed for sampling, updating and executing by relying on the latest sampled information. Then, the switching surface and the related dynamics of the original MJSs are obtained by means of a self-triggered sampling scheme. To guarantee both the system stability and the desired disturbance attenuation performance, sufficient conditions are presented in terms of linear matrix inequalities. Moreover, to ensure the time finiteness of the predefined switching surface reachability and satisfy the desirable sliding motion performance, an SMC law is proposed. The validity and superiority of the developed scheme are demonstrated via a simulation example.
