1,256 publications from this institution
This paper presents a comparison between two modeling approaches of vehicle to pole collision. Firstly, analytical and curve fitting methods are explained and subsequently they are utilized to create lumped parameter models. Having parameters of such systems and their responses we proceed to brief description of the radial basis function neural network and its application to the linear models' coefficients' identification. Comparative analysis of the models formulated according to those two different manners is done. (6 pages)
This paper addresses the problem of sliding mode control (SMC) for a type of uncertain time-delay nonlinear descriptor systems represented by T-S fuzzy models. One crucial contributing factor is to put forward a novel integral fuzzy switching manifold involved with time delay. Compared with previous results, the key benefit of the new manifold is that the input matrices via different subsystems are permitted to be diverse, and thus much more applicability will be achieved. By resorting to Frobenius' theorem and double orthogonal complement, the existence condition of the fuzzy manifold is presented. The admissibility conditions of sliding motion with a strictly dissipative performance are further provided. Then, the desired fuzzy SMC controller is synthesized by analyzing the reachability of the manifold. Moreover, an adaptive fuzzy SMC controller is also proposed to adapt the input saturation and the matched uncertainty with unknown upper bounds. The feasibility and virtue of our theoretical findings are demonstrated by a fuzzy SMC controller implementation for a practical system about the pendulum.
We deal with the design problem of minimum entropy ℋ ∞ filter in terms of linear matrix inequality (LMI) approach for linear continuous-time systems with a state-space model subject to parameter uncertainty that belongs to a given convex bounded polyhedral domain. Given a stable uncertain linear system, our attention is focused on the design of full-order and reduced-order robust minimum entropy ℋ ∞ filters, which guarantee the filtering error system to be asymptotically stable and are required to minimize the filtering error system entropy (at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi mathvariant="normal">s</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>∞</mml:mi></mml:math>) and to satisfy a prescribed ℋ ∞ disturbance attenuation performance. Sufficient conditions for the existence of desired full-order and reduced-order filters are established in terms of LMIs, respectively, and the corresponding filter synthesis is cast into a convex optimization problem which can be efficiently handled by using standard numerical software. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method.
This paper is concerned with the issues of passivity analysis and dynamic output feedback (DOF) passive control for uncertain switched stochastic systems with time-varying delay via multiple storage functions (MSFs) method. Firstly, based on the MSFs method, a sufficient condition for the existence of the passivity of the underlying system is established in terms of linear matrix inequalities (LMIs). Furthermore, the problem of dynamic output feedback passive control is investigated. Based on the obtained passivity condition, a sufficient condition for the existence of the desired switched passive controller is derived. 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.
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.
This paper investigates the problem of designing a robust H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> repetitive control system with periodic time-delay. Firstly, a continuous-discrete two-dimensional (2D) model with time-delay is constructed to describe the control and learning actions of the system. Then, by choosing an appropriate Lyapunov functional, LMI-based sufficient stability conditions for asymptotic stability and satisfactory H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> tracking performance of the 2D model is derived. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
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.
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.
How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn, store, and evaluate Mahalanobis matrix efficiently. Besides, a dynamic triplets building strategy is proposed to build a feedback from the obtained Mahalanobis matrix to the triplet constraints, which can further improve the LDMLT algorithm. Furthermore, the proposed method is applied to various applications, including pattern recognition, facial expression recognition, and image retrieval. The results demonstrate the improved performance of the proposed approach.
This chapter addresses the problem of sliding mode control (SMC) in fractional-order complex networked systems (FO-CNS) under stochastic network attacks by designing a nonfragile observer frame. The proposed approach utilizes a combination of event-triggered techniques. First, a nonfragile fractional-order state observer is developed, enabling the design of a fractional-order integral sliding surface function. Next, a combination event-triggered condition (CETC) is introduced, utilizing estimated observer errors and sliding mode error vectors. By solving linear matrix inequalities (LMIs), several sufficient conditions are obtained to guarantee the stability of the closed-loop systems. Additionally, an improved self-triggered condition is designed to prevent Zeno behavior. This condition relies on a predefined event-triggered mechanism. The Gronwall–Bellman inequality is employed to determine a positive lower bound of the trigger sequence, ensuring the avoidance of infinite triggering within a finite time interval. Finally, two numerical simulations are presented to demonstrate the effectiveness and feasibility of the proposed method.
This paper deals with the observer design for Takagi-Sugeno (T-S) fuzzy models subject to unknown inputs and disturbance affecting both states and outputs of the system. Sufficient conditions to design an unknown input T-S observer are given in Linear Matrix Inequalities (LMIs) terms. Relaxations are introduced by using intermediate variables. Numerical example is given to illustrate the effectiveness of the given result.
This paper will entail the work of modeling, testing and verification of the Dielectric Electro Active Polymers (DEAP's) material. Whereby mathematical model will be derived to describe the statics and the dynamics of the DEAP's sheet, parameters of the models are determined using experimental/simulation results which will be used to verify and determine the precision of the derived dynamic model. The testing for finding the material properties is requested before doing the dynamic analysis, however this will not be addressed thoroughly in this paper. Both material characterization and dynamic analysis tests are performed using the developed testing rig specially designed for this purpose. The results of this study highlight the dependency of the material dynamics on the mechanical fixture of the material as well as the material creep properties, whereby the range of operation can be reduced to lower frequencies or expanded to higher frequencies when the mechanical fixture is designed for a certain application. The test results for the material shows an offset error between 0–20 Hz mainly because of the material creep but the error diverge after this range stimulated when exceeding the natural frequency of the system which lead to non stable state effecting the controllability of the actuated material, the offset error due to the creep is found to be eliminated using faster discharging circuits.
Existing controller designs for pendulous accelerometers focus on maintaining angular balance, emphasizing low-frequency measurement performance while neglecting high-frequency dynamic control accuracy. However, in high dynamic conditions, dynamic control errors directly impact accelerometer measurement performance. Through mechanical analysis, residual torque on the pendulum is identified as a major contributor to dynamic control errors in pendulous accelerometers. To enhance the dynamic performance, a torque-balance Linear Quadratic Regulator (LQR) control method is proposed, aiming to suppress dynamic control errors by implementing torque balance control. The effectiveness and stability of this control method is demonstrated through simulations and experiments. Furthermore, this study compares the advantages of torque-balance LQR closed-loop systems in dynamic compensation. The results illustrate that the proposed method significantly enhances accelerometer dynamic control performance and compensated dynamic accuracy, thereby increasing reliability and accuracy in high dynamic conditions.
1 Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway 2 College of Automation, Harbin Engineering University, Nantong Street #145, Nangang District, Harbin, China 3 School of Astronautics, Harbin Institute of Technology, P.O. Box 3015, Yikuang Street #2, Nangang District, Harbin, China 4 Laboratory of Modeling, Information & Systems, University of Picardie Jules Verne, Amiens, France
Summary In this paper, several new Razumikhin‐type theorems for impulsive stochastic functional differential equations are studied by applying stochastic analysis techniques and Razumikhin stability approach. By developing a new comparison principle for stochastic version, some novel criteria of the p th moment integral input‐to‐state stability and input‐to‐state stability are derived for the related systems. The feature of the criteria shows that time‐derivatives of the Razumikhin functions are allowed to be indefinite, even unbounded, which can loosen the constraints of the existing results. Finally, some examples are given to illustrate the usefulness and significance of the theoretical results.
