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Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

Wilson ED, Assaf T, Pearson MJ, Rossiter JM, Dean P, Anderson SR, Porrill J - Front Neurorobot (2015)

Bottom Line: The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise.It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation.Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control.

View Article: PubMed Central - PubMed

Affiliation: Sheffield Robotics, University of Sheffield , Sheffield , UK.

ABSTRACT
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

No MeSH data available.


Experimental adaptive control of different DEAPs using cerebellar-inspired control. Vertical lines indicate when learning starts. (A) Learned weights when controlling actuator 1 (see Table 2). (B) Learned weights when controlling actuator 4 (see Table 2). (C) RMS errors over time during experiment for actuator 1 (), actuator 4 (), and actuator 7 ().
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Figure 5: Experimental adaptive control of different DEAPs using cerebellar-inspired control. Vertical lines indicate when learning starts. (A) Learned weights when controlling actuator 1 (see Table 2). (B) Learned weights when controlling actuator 4 (see Table 2). (C) RMS errors over time during experiment for actuator 1 (), actuator 4 (), and actuator 7 ().

Mentions: Results from three different actuators are given in Figure 5. Two of these were actuators used in the estimate of the brainstem model, and the other a different actuator (see also Figure 4). For each DEAP, the weights adjust to minimize errors in the displacement tracking. Errors in tracking the displacement response for actuator 4 are slightly larger. The average applied voltages (prior to amplification) at the end of learning were 2.4, 2.9, and 2.3 V for actuators 1, 4, and 7, respectively. Inputs to actuator 4 are larger than those to the other actuators and above the range for which the behavior can be approximated as linear. The increased errors in tracking the displacement response for actuator 4 are likely to be caused by the actuator operating in its non-linear region during experiments.


Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

Wilson ED, Assaf T, Pearson MJ, Rossiter JM, Dean P, Anderson SR, Porrill J - Front Neurorobot (2015)

Experimental adaptive control of different DEAPs using cerebellar-inspired control. Vertical lines indicate when learning starts. (A) Learned weights when controlling actuator 1 (see Table 2). (B) Learned weights when controlling actuator 4 (see Table 2). (C) RMS errors over time during experiment for actuator 1 (), actuator 4 (), and actuator 7 ().
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4507459&req=5

Figure 5: Experimental adaptive control of different DEAPs using cerebellar-inspired control. Vertical lines indicate when learning starts. (A) Learned weights when controlling actuator 1 (see Table 2). (B) Learned weights when controlling actuator 4 (see Table 2). (C) RMS errors over time during experiment for actuator 1 (), actuator 4 (), and actuator 7 ().
Mentions: Results from three different actuators are given in Figure 5. Two of these were actuators used in the estimate of the brainstem model, and the other a different actuator (see also Figure 4). For each DEAP, the weights adjust to minimize errors in the displacement tracking. Errors in tracking the displacement response for actuator 4 are slightly larger. The average applied voltages (prior to amplification) at the end of learning were 2.4, 2.9, and 2.3 V for actuators 1, 4, and 7, respectively. Inputs to actuator 4 are larger than those to the other actuators and above the range for which the behavior can be approximated as linear. The increased errors in tracking the displacement response for actuator 4 are likely to be caused by the actuator operating in its non-linear region during experiments.

Bottom Line: The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise.It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation.Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control.

View Article: PubMed Central - PubMed

Affiliation: Sheffield Robotics, University of Sheffield , Sheffield , UK.

ABSTRACT
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

No MeSH data available.