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A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.

Wright J, Macefield VG, van Schaik A, Tapson JC - Front Neurosci (2016)

Bottom Line: It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices.Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback.The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, University of Western Sydney Sydney, NSW, Australia.

ABSTRACT
It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

No MeSH data available.


Internal Model Control. The inclusion of a model of the Plant allows for the Controller to incorporate some of the dynamics of the system into the control policy.
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Figure 3: Internal Model Control. The inclusion of a model of the Plant allows for the Controller to incorporate some of the dynamics of the system into the control policy.

Mentions: Internal Model Control (IMC) is an approach to feedback controller design that incorporates a model of the system that is being controlled (García et al., 1989). The model can be developed based only on the relationship between the inputs and outputs of the system, or alternatively a partial model or complete model of the system can be utilized (LeDuc et al., 2011). At each time step the internal model is evaluated forward to a horizon, offering a prediction of the system behavior in response to the controller's input, and the control inputs are evaluated against a cost function to find the optimum command to be executed at the next time step (Pan et al., 2015). A block diagram illustrates IMC as Figure 3.


A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.

Wright J, Macefield VG, van Schaik A, Tapson JC - Front Neurosci (2016)

Internal Model Control. The inclusion of a model of the Plant allows for the Controller to incorporate some of the dynamics of the system into the control policy.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940409&req=5

Figure 3: Internal Model Control. The inclusion of a model of the Plant allows for the Controller to incorporate some of the dynamics of the system into the control policy.
Mentions: Internal Model Control (IMC) is an approach to feedback controller design that incorporates a model of the system that is being controlled (García et al., 1989). The model can be developed based only on the relationship between the inputs and outputs of the system, or alternatively a partial model or complete model of the system can be utilized (LeDuc et al., 2011). At each time step the internal model is evaluated forward to a horizon, offering a prediction of the system behavior in response to the controller's input, and the control inputs are evaluated against a cost function to find the optimum command to be executed at the next time step (Pan et al., 2015). A block diagram illustrates IMC as Figure 3.

Bottom Line: It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices.Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback.The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, University of Western Sydney Sydney, NSW, Australia.

ABSTRACT
It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

No MeSH data available.