Adaptive Controller
Adaptive Controller - Related Videos
11:12
Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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Cited by 27 •
2014
Adaptive deep brain stimulation (aDBS) is effective for Parkinson’s disease, improving symptoms and reducing power consumption compared to conventional deep brain stimulation (cDBS). In aDBS we track a local field potential biomarker (beta oscillatory amplitude) in real time and use this to control the timing of stimulation.
11:38
Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization
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Cited by 9 •
2013
A method is discussed by which the in vivo mechanical behavior of stimuli-responsive materials is monitored as a function of time. Samples are tested ex vivo using a microtensile tester with environmental controls to simulate the physiological environment. This work further promotes understanding the in vivo behavior of our material.
11:30
A Molecular Readout of Long-term Olfactory Adaptation in C. elegans
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Cited by 2 •
2012
Here we describe a molecular readout of long-term olfactory adaptation in Caenorhabditis elegans. The Protein Kinase G, EGL-4, is necessary for stable adaptation responses in the primary sensory neuron pair called AWC. During prolonged odor exposure EGL-4 translocates from the cytosol to nucleus of the AWC.
02:52
Neuronal Adaptation to Repetitive Hypoxic Preconditioning in Mice
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2025
Source: Poinsatte, K., et al., Quantification of Neurovascular Protection Following Repetitive Hypoxic Preconditioning and Transient Middle Cerebral Artery Occlusion in Mice. J. Vis. Exp. (2015)This video demonstrates the method of repetitive hypoxic preconditioning (RHP) by exposing a mouse to controlled hypoxia cycles before inducing an ischemic stroke. The experiment evaluates its effects on blood-brain barrier stability, neuronal adaptation, and inflammation.
08:16
Adaptation of a Haptic Robot in a 3T fMRI
0 Views •
Cited by 2 •
2011
The adaptation and use of a haptic robot in a 3T fMRI is described.
FAQs
Online estimation helps the system update its understanding of changing system dynamics while operation continues. Rather than relying only on parameters selected in advance, the controller uses incoming measurements to revise its control strategy and reduce error. This is particularly important when neural signals or behavioral states change, because the relationship between measured activity and intended control may not remain constant.
The controller compares the measured output with a desired state to identify the current error. That error provides the basis for adjusting control parameters, allowing subsequent commands to better match the target. Repeating this measurement, comparison, and adjustment cycle supports continued correction instead of a single fixed response, which can help preserve control performance as conditions evolve.
Neural signals can vary during operation, and behavioral states may also change the system’s response. An adaptive controller accounts for these shifts by modifying its strategy rather than assuming that one signal-to-control relationship will remain stable. This responsiveness can improve control stability and help personalize device operation for the changing neural and behavioral conditions of an individual user.
A fixed strategy applies control parameters without continually revising them in response to new system measurements. An adaptive controller instead uses feedback to update those parameters as the system, environment, or target changes. This distinction matters when signal variability would otherwise reduce performance, because ongoing adjustment can maintain a closer relationship between the desired state and the observed output.
The process requires measurements of system output, a specified desired state, and a way to compare the two. The controller then identifies the resulting error, updates its control parameters, and applies the revised strategy. Repeating this feedback cycle allows the system to respond during operation rather than waiting for a later manual recalibration or redesign.
In a closed-loop brain-machine interface, neural signals and resulting system behavior provide measurements that can guide ongoing control updates. The controller can respond to changing neural activity or behavioral state while adjusting operation toward the intended target. This supports more stable interaction between brain signals and device control, which is important when signal characteristics vary during use.
Adaptive controllers can adjust neuroprosthesis operation or neural stimulation as neural signals and behavioral states change. Their feedback-based updates support personalized device operation and can help maintain performance despite signal variability. These capabilities make the approach relevant to assistive technologies, where consistent control may depend on responding to the individual’s changing neural state rather than using unchanged parameters.
Adaptive control provides a framework for studying how neural signals relate to intended actions and changing behavioral states. By updating control parameters in response to measured outcomes, researchers can examine neural control under ongoing feedback rather than only under fixed conditions. The same framework also connects basic investigation of neural control with development of practical assistive technologies.