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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.


Feedforward and Feedback Control. Feedforward or open-loop control is shown here in the solid line. The controller generates a command that is applied to the system, or Plant. In response to the command the system performs an action at the Output. Closed-loop or feedback control is achieved by the inclusion of the Sensor component, shown here as the dashed line. The Sensor measures the Output enabling the Controller to assess the error and adjust the next Input to the Plant.
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Figure 2: Feedforward and Feedback Control. Feedforward or open-loop control is shown here in the solid line. The controller generates a command that is applied to the system, or Plant. In response to the command the system performs an action at the Output. Closed-loop or feedback control is achieved by the inclusion of the Sensor component, shown here as the dashed line. The Sensor measures the Output enabling the Controller to assess the error and adjust the next Input to the Plant.

Mentions: Feedforward or open-loop control generates a command for the plant that is expected to produce the correct output. However, there is no measurement of the output from the plant, and hence no measurement of error, so the controller has no mechanism to modulate a command (Houk, 1988). A block diagram of open-loop control is shown as Figure 2. Implicit within open-loop control is the assumption of a perfectly described system that can be used to generate a control. Leaving aside the difficulties in creating a perfect model of any system, open-loop approaches do not take noise or measurement error into account.


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

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

Feedforward and Feedback Control. Feedforward or open-loop control is shown here in the solid line. The controller generates a command that is applied to the system, or Plant. In response to the command the system performs an action at the Output. Closed-loop or feedback control is achieved by the inclusion of the Sensor component, shown here as the dashed line. The Sensor measures the Output enabling the Controller to assess the error and adjust the next Input to the Plant.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Feedforward and Feedback Control. Feedforward or open-loop control is shown here in the solid line. The controller generates a command that is applied to the system, or Plant. In response to the command the system performs an action at the Output. Closed-loop or feedback control is achieved by the inclusion of the Sensor component, shown here as the dashed line. The Sensor measures the Output enabling the Controller to assess the error and adjust the next Input to the Plant.
Mentions: Feedforward or open-loop control generates a command for the plant that is expected to produce the correct output. However, there is no measurement of the output from the plant, and hence no measurement of error, so the controller has no mechanism to modulate a command (Houk, 1988). A block diagram of open-loop control is shown as Figure 2. Implicit within open-loop control is the assumption of a perfectly described system that can be used to generate a control. Leaving aside the difficulties in creating a perfect model of any system, open-loop approaches do not take noise or measurement error into account.

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.