The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform

B Hudgins - One of the best experts on this subject based on the ideXlab platform.

  • a robust real time Control scheme for multifunction myoelectric Control
    IEEE Transactions on Biomedical Engineering, 2003
    Co-Authors: Kevin Englehart, B Hudgins
    Abstract:

    This paper represents an ongoing investigation of dexterous and natural Control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic Control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural Control Actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded Control systems.

  • a robust real time Control scheme for multifunction myoelectric Control
    IEEE Transactions on Biomedical Engineering, 2003
    Co-Authors: Kevin Englehart, B Hudgins
    Abstract:

    This paper represents an ongoing investigation of dexterous and natural Control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic Control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural Control Actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded Control systems.

Kevin Englehart - One of the best experts on this subject based on the ideXlab platform.

  • continuous myoelectric Control for powered prostheses using hidden markov models
    IEEE Transactions on Biomedical Engineering, 2005
    Co-Authors: Adrian D C Chan, Kevin Englehart
    Abstract:

    This paper represents an ongoing investigation of dexterous and natural Control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural Control Actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.

  • a robust real time Control scheme for multifunction myoelectric Control
    IEEE Transactions on Biomedical Engineering, 2003
    Co-Authors: Kevin Englehart, B Hudgins
    Abstract:

    This paper represents an ongoing investigation of dexterous and natural Control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic Control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural Control Actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded Control systems.

  • a robust real time Control scheme for multifunction myoelectric Control
    IEEE Transactions on Biomedical Engineering, 2003
    Co-Authors: Kevin Englehart, B Hudgins
    Abstract:

    This paper represents an ongoing investigation of dexterous and natural Control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic Control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural Control Actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded Control systems.

Ira B Schwartz - One of the best experts on this subject based on the ideXlab platform.

  • set based corral Control in stochastic dynamical systems making almost invariant sets more invariant
    Chaos, 2011
    Co-Authors: Eric Forgoston, Lora Billings, Philip Yecko, Ira B Schwartz
    Abstract:

    We consider the problem of stochastic prediction and Control in a time-dependent stochastic environment, such as the ocean, where escape from an almost invariant region occurs due to random fluctuations. We determine high-probability Control-Actuation sets by computing regions of uncertainty, almost invariant sets, and Lagrangian coherent structures. The combination of geometric and probabilistic methods allows us to design regions of Control, which provide an increase in loitering time while minimizing the amount of Control Actuation. We show how the loitering time in almost invariant sets scales exponentially with respect to the Control Actuation, causing an exponential increase in loitering times with only small changes in Actuation force. The result is that the Control Actuation makes almost invariant sets more invariant.

Panagiotis D. Christofides - One of the best experts on this subject based on the ideXlab platform.

  • dynamic output feedback covariance Control of stochastic dissipative partial differential equations
    Chemical Engineering Science, 2008
    Co-Authors: Yiming Lou, Panagiotis D. Christofides
    Abstract:

    Abstract In this work, we develop a method for dynamic output feedback covariance Control of the state covariance of linear dissipative stochastic partial differential equations (PDEs) using spatially distributed Control Actuation and sensing with noise. Such stochastic PDEs arise naturally in the modeling of surface height profile evolution in thin film growth and sputtering processes. We begin with the formulation of the stochastic PDE into a system of infinite stochastic ordinary differential equations (ODEs) by using modal decomposition. A finite-dimensional approximation is then obtained to capture the dominant mode contribution to the surface roughness profile (i.e., the covariance of the surface height profile). Subsequently, a state feedback Controller and a Kalman–Bucy filter are designed on the basis of the finite-dimensional approximation. The dynamic output feedback covariance Controller is subsequently obtained by combining the state feedback Controller and the state estimator. The steady-state expected surface covariance under the dynamic output feedback Controller is then estimated on the basis of the closed-loop finite-dimensional system. An analysis is performed to obtain a theoretical estimate of the expected surface covariance of the closed-loop infinite-dimensional system. Applications of the linear dynamic output feedback Controller to both the linearized and the nonlinear stochastic Kuramoto–Sivashinsky equations (KSEs) are presented. Finally, nonlinear state feedback Controller and nonlinear output feedback Controller designs are also presented and applied to the nonlinear stochastic KSE.

  • Predictive Control of parabolic PDEs with boundary Control Actuation
    Chemical Engineering Science, 2006
    Co-Authors: Stevan Dubljevic, Panagiotis D. Christofides
    Abstract:

    This work focuses on predictive Control of linear parabolic partial differential equations (PDEs) with boundary Control Actuation subject to input and state constraints. Under the assumption that measurements of the PDE state are available, various finite-dimensional and infinite-dimensional predictive Control formulations are presented and their ability to enforce stability and constraint satisfaction in the infinite-dimensional closed-loop system is analyzed. A numerical example of a linear parabolic PDE with unstable steady state and flux boundary Control subject to state and Control constraints is used to demonstrate the implementation and effectiveness of the predictive Controllers.

V Venkatasubramanian - One of the best experts on this subject based on the ideXlab platform.

  • wide area optimal Control of electric power systems with application to transient stability for higher order contingencies
    IEEE Transactions on Power Systems, 2013
    Co-Authors: Gregary C Zweigle, V Venkatasubramanian
    Abstract:

    A real-time stabilizing Control method for responding to N-K contingencies, with large K, is developed utilizing network and machine time-synchronized measurements. The Controls follow an optimality principle in driving rotor-angles to an acceptable equilibrium point, at minimum cost, by predicting state response trajectory to a collection of stepped structural changes, from an admissible set, according to a defined model. A cost metric suitable for mitigating rotor-angle instability is developed. Non-idealities in modeling, measurement latency, Control availability, and Actuation success are investigated. It is shown how Control over system structure in a feedback formulation increases the capability to handle higher order contingencies. As an experimental example, a set of simultaneous N-3 transient stability related contingencies are stabilized for the IEEE 39-bus system. Furthermore, the response after Control Actuation failure is investigated and it is shown that the system remains driven to a valid stable equilibrium point.