The Experts below are selected from a list of 27057 Experts worldwide ranked by ideXlab platform
Miroslav Krstic - One of the best experts on this subject based on the ideXlab platform.
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time varying Feedback for regulation of normal form nonlinear systems in prescribed finite time
Automatica, 2017Co-Authors: Yongduan Song, Yujuan Wang, John Holloway, Miroslav KrsticAbstract:Abstract While non-smooth approaches (including sliding mode control) provide Explicit Feedback laws that ensure finite-time stabilization but in terminal time that depends on the initial condition, fixed-time optimal control with a terminal constraint ensures regulation in prescribed time but lacks the Explicit character in the presence of nonlinearities and uncertainties. In this paper we present an alternative to these approaches, which, while lacking optimality, provides Explicit time-varying Feedback laws that achieve regulation in prescribed finite time, even in the presence of non-vanishing (though matched) uncertain nonlinearities. Our approach employs a scaling of the state by a function of time that grows unbounded towards the terminal time and is followed by a design of a controller that stabilizes the system in the scaled state representation, yielding regulation in prescribed finite time for the original state. The achieved robustness to right-hand-side disturbances is not accompanied by robustness to measurement noise, which is also absent from all controllers that are nonsmooth or discontinuous at the origin.
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marcum q functions and Explicit Feedback laws for stabilization of constant coefficient 2 2 linear hyperbolic systems
Conference on Decision and Control, 2013Co-Authors: Rafael Vazquez, Miroslav KrsticAbstract:We find the exact analytical solution to a Goursat PDE system governing the kernels of a backstepping-based boundary control law that stabilizes a constant-coefficient 2 × 2 system of first-order hyperbolic linear PDEs. The solution to the Goursat system is related to the solution of a simpler, Explicitly solvable Goursat system through a suitable infinite series of powers of partial derivatives which is summed Explicitly in terms of special functions, including Bessel functions and the generalized Marcum Q-functions of the first order. The Marcum functions are common in certain applications in communications but have not appeared previously in control design problems.
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delay adaptive Feedback for linear feedforward systems
Systems & Control Letters, 2010Co-Authors: Nikolaos Bekiarisliberis, Miroslav KrsticAbstract:Abstract Predictor techniques are an indispensable part of the control design toolbox for plants with input and state delays of significant size. Yet, they suffer from sensitivity to the design values. Explicit Feedback laws were recently introduced by Jankovic for a class of feedforward linear systems with simultaneous state and input delays. For the case where the delays are of unknown length, using the certainty equivalence principle, we design a Lyapunov-based adaptive controller, which achieves global stability and regulation, for arbitrary initial estimates for the delays. We consider a two-block sub-class of linear feedforward systems. A generalization to the n -block case involves a recursive application of the same techniques.
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compensating a string pde in the actuation or sensing path of an unstable ode
American Control Conference, 2009Co-Authors: Miroslav KrsticAbstract:How to control an unstable linear system with a long pure delay in the actuator path? This question was resolved using ‘predictor’ or ‘finite spectrum assignment’ designs in the 1970s. Here we address a more challenging question: How to control an unstable linear system with a wave PDE in the actuation path? Physically one can think of this problem as having to stabilize a system to whose input one has access through a string. The challenges of overcoming string/wave dynamics in the actuation path include their infinite dimension, finite propagation speed of the control signal, and the fact that all of their (infinitely many) eigenvalues are on the imaginary axis. In this paper we provide an Explicit Feedback law that compensates the wave PDE dynamics at the input of an LTI ODE and stabilizes the overall system. In addition, we prove robustness of the Feedback to the error in a priori knowledge of the propagation speed in the wave PDE. Finally, we consider a dual problem where the wave PDE is in the sensing path and design an exponentially convergent observer.
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Compensating a String PDE in the Actuation or Sensing Path of an Unstable ODE
IEEE Transactions on Automatic Control, 2009Co-Authors: Miroslav KrsticAbstract:How to control an unstable linear system with a long pure delay in the actuator path? This question was resolved using 'predictor' or 'finite spectrum assignment' designs in the 1970s. Here we address a more challenging question: How to control an unstable linear system with a wave partial differential equation (PDE) in the actuation path? Physically one can think of this problem as having to stabilize a system to whose input one has access through a string. The challenges of overcoming string/wave dynamics in the actuation path include their infinite dimension, finite propagation speed of the control signal, and the fact that all of their (infinitely many) eigenvalues are on the imaginary axis. In this technical note we provide an Explicit Feedback law that compensates the wave PDE dynamics at the input of an linear time-invariant ordinary differential equation and stabilizes the overall system. In addition, we prove robustness of the Feedback to the error in a priori knowledge of the propagation speed in the wave PDE. Finally, we consider a dual problem where the wave PDE is in the sensing path and design an exponentially convergent observer.
Strahinja Dosen - One of the best experts on this subject based on the ideXlab platform.
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myocontrol is closed loop control incidental Feedback is sufficient for scaling the prosthesis force in routine grasping
Journal of Neuroengineering and Rehabilitation, 2018Co-Authors: Marko Markovic, Strahinja Dosen, Meike A Schweisfurth, Leonard F EngelsAbstract:Sensory Feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in upper-limb prosthetics by providing artificial sensory Feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory Feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory Feedback, the baseline open-loop performance needs to be established. The myoelectric control of naive able-bodied subjects was evaluated during modulation of electromyographic signals (EMG task), and grasping with a prosthesis (Prosthesis task). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit Feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with Explicit visual force Feedback. The subjects’ ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target. The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit Feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional Feedback. However, the increase in implicit Feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of Explicit Feedback. Explicit Feedback improved the performance mainly at the higher target-force levels. The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional Feedback, naive subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of Feedback information.
Yongtae Park - One of the best experts on this subject based on the ideXlab platform.
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application level frequency control of periodic safety messages in the ieee wave
IEEE Transactions on Vehicular Technology, 2012Co-Authors: Yongtae Park, Hyogon KimAbstract:The basic safety message (BSM, also called a “beacon”) is the most fundamental building block that enables proximity awareness in the IEEE Wireless Access in Vehicular Environments. For driving safety and agile networking, the frequency of the BSM transmissions should be maintained at the maximum allowable level, but at the same time, rampant BSM proliferation needs to be curbed to leave room for higher priority messages and other applications that share what small bandwidth we have in the 5.9-GHz Dedicated Short-Range Communications band. In this paper, we describe an application-level messaging frequency estimation scheme called frequency adjustment with random epochs (FARE), which significantly improves the BSM throughput while using less bandwidth than the bare 802.11p delivery. FARE can be implemented purely on the application layer and uses neither cross-layer optimization nor Explicit Feedback from neighboring vehicles. It is only a few lines of code; therefore, it can easily be embedded in the BSM application program executed in the onboard unit.
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Q-rater: A collaborative reputation system based on source credibility theory
Expert Systems with Applications, 2009Co-Authors: Jinhyung Cho, Kwiseok Kwon, Yongtae ParkAbstract:The consumer is an important information source and the after-transaction-Feedback is used as the quality indicator for trust building in e-commerce. However, the possibility of incorrect information from unreliable users and declining trust in the electronic market looms large. Hence, there is a need for a fairer and more objective reputation mechanism. Most existing reputation systems focus on the reputation of users than items and they rely on Explicit Feedback information. As a solution to these problems, we propose a reputation system ("Q-rater") suitable for B2C e-commerce. We adopt the source credibility theory of consumer psychology and the basic mechanism of collaborative filtering methods for the overall process of the proposed reputation system. We also evaluate performance of the Q-rater experimentally by comparing it with the other benchmark systems and observing the performance changes with variations in the size of rater group and item rating aggregation mechanism. © 2008.
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a time based approach to effective recommender systems using implicit Feedback
Expert Systems With Applications, 2008Co-Authors: Tong Queue Lee, Young Park, Yongtae ParkAbstract:Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using Explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect Explicit Feedback data; only implicit Feedback is available. In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without Explicit Feedback data. Our method constructs pseudo rating data from the implicit Feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user's purchase time and the item's launch time in order to increase recommendation accuracy. Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system.
Yehuda Koren - One of the best experts on this subject based on the ideXlab platform.
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Collaborative filtering for implicit Feedback datasets
Proceedings - IEEE International Conference on Data Mining ICDM, 2008Co-Authors: Yifan Hu, Chris Volinsky, Yehuda KorenAbstract:A common task of recommender systems is to improve customer experience through personalized recommenda-tions based on prior implicit Feedback. These systems pas-sively track different sorts of user behavior, such as pur-chase history, watching habits and browsing activity, in or-der to model user preferences. Unlike the much more ex-tensively researched Explicit Feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique proper-ties of implicit Feedback datasets. We propose treating the data as indication of positive and negative preference asso-ciated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feed-back recommenders. We also suggest a scalable optimiza-tion procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.
Marko Markovic - One of the best experts on this subject based on the ideXlab platform.
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myocontrol is closed loop control incidental Feedback is sufficient for scaling the prosthesis force in routine grasping
Journal of Neuroengineering and Rehabilitation, 2018Co-Authors: Marko Markovic, Strahinja Dosen, Meike A Schweisfurth, Leonard F EngelsAbstract:Sensory Feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in upper-limb prosthetics by providing artificial sensory Feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory Feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory Feedback, the baseline open-loop performance needs to be established. The myoelectric control of naive able-bodied subjects was evaluated during modulation of electromyographic signals (EMG task), and grasping with a prosthesis (Prosthesis task). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit Feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with Explicit visual force Feedback. The subjects’ ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target. The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit Feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional Feedback. However, the increase in implicit Feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of Explicit Feedback. Explicit Feedback improved the performance mainly at the higher target-force levels. The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional Feedback, naive subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of Feedback information.
