A simplified model predictive control algorithm is designed for discrete-time Markov jump systems with mixed uncertainties. The mixed uncertainties include model polytope uncertainty and partly unknown transition probability. The simplified algorithm involves finite steps. Firstly, in the previous steps, a simplified mode-dependent predictive controller is presented to drive the state to the neighbor area around the origin. Then the trajectory of states is driven as expected to the origin by the final-step mode-independent predictive controller. The computational burden is dramatically cut down and thus it costs less time but has the acceptable dynamic performance. Furthermore, the polyhedron invariant set is utilized to enlarge the initial feasible area. The numerical example is provided to illustrate the efficiency of the developed results.
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.
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.
Compared with fixed-bottom installation, deep water floating wind turbine has to undergo more severe structural loads due to extra degrees of freedom. Aiming for effective load reduction, this paper deals with the evaluation of a passive structural control design for a spar-type floating wind turbine, and the proposed strategy is to install a tuned mass damper (TMD) into the spar platform. Firstly, a mathematical model for wind turbine surge-heave-pitch motion is established based on the D’Alembert’s principle of inertial forces. Then, parameter estimation is performed by comparing the outputs from the proposed model and the state-of-the-art simulator. Further, different optimization methods are adopted to optimize TMD parameters when considering different performance indices. Finally, high fidelity nonlinear simulations with previous optimized TMD designs are conducted under different wind and wave conditions. Simulation results demonstrate both the effectiveness and limitation of different TMD parameter choices, providing parametric analysis and design basis for future improvement on floating wind turbine load reduction with structural control methods.
No AccessJournal of Speech, Language, and Hearing ResearchErratum1 Apr 2014Erratumis erratum ofUsing Statistical Process Control Charts to Study Stuttering Frequency Variability During a Single Day Hamid Karimi, Sue O'Brian, Mark Onslow, Mark Jones, Ross Menzies, and Ann Packman Hamid Karimi Google Scholar More articles by this author , Sue O'Brian Google Scholar More articles by this author , Mark Onslow Google Scholar More articles by this author , Mark Jones Google Scholar More articles by this author , Ross Menzies Google Scholar More articles by this author and Ann Packman Google Scholar More articles by this author https://doi.org/10.1044/2014_JSLHR-S-14-0069 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In Additional Resources FiguresReferencesRelatedDetailsRelated articlesUsing Statistical Process Control Charts to Study Stuttering Frequency Variability During a Single Day Volume 57Issue 2April 2014Pages: 705-705 Get Permissions Add to your Mendeley library History Published in issue: Apr 1, 2014 Metrics Topicsasha-topicsasha-article-typesleader-topicsCopyright & PermissionsCopyright © 2014 American Speech-Language-Hearing AssociationPDF downloadLoading ...
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 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.
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This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. Due to its excellent performance in generalization, the classification performance of SVM is satisfactory. SVM algorithm combined with kernel function has the nonlinear attribute and can better handle the case where samples and attributes are massive. In addition, with forehand optimizing the parameters using the cross-validation technique, SVM can produce high accuracy in fault detection. Therefore, there is no need to deal with original data or refer to other algorithms, making the classification problem simple to handle. In order to further illustrate the efficiency, an industrial benchmark of Tennessee Eastman (TE) process is utilized with the SVM algorithm and PLS algorithm, respectively. By comparing the indices of detection performance, the SVM technique shows superior fault detection ability to the PLS algorithm.
In this study, a model predictive control (MPC)‐based fault tolerant control (FTC) design method for air‐breathing hypersonic vehicles (AHVs) is proposed. The non‐linear model of AHVs is recalled and the FTC problem is discussed. The problem is challenging because the relationship between flight dynamics and the control input is not so clear. For the purpose of non‐linear FTC, an optimisation‐based reference reconfiguration method is given. Through the reconfiguration, a new reference command, which can be realised by the faulty system, is constructed. Actuator saturation has been take into account when constructing the new reference command. An MPC‐based controller reconfiguration method is proposed with respect to the new command and system faults. Finally, simulations are given to show the effectiveness of the proposed control 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.
On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On the other hand, our understanding of the language of fake news is still minimal. Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured. Nevertheless, this has rarely been investigated in the fake news detection domain and faces tremendous challenges. First, existing methods for capturing discourse-level structure rely on annotated corpora which are not available for fake news datasets. Second, how to extract out useful information from such discovered structures is another challenge. To address these challenges, we propose Hierarchical Discourse-level Structure for Fake news detection. HDSF learns and constructs a discourse-level structure for fake/real news articles in an automated and data-driven manner. Moreover, we identify insightful structure-related properties, which can explain the discovered structures and boost our understating of fake news. Conducted experiments show the effectiveness of the proposed approach. Further structural analysis suggests that real and fake news present substantial differences in the hierarchical discourse-level structures.
