Adaptive Feedforward Model Predictive Control for Torque Generation Through Asynchronous Intrafascicular Multi-Electrode Stimulation
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View abstract on PubMed
Summary
This summary is machine-generated.An adaptive feedforward model predictive controller (aF-MPC) improves asynchronous intrafascicular multi-electrode stimulation (aIFMS) for precise isometric torque control. This new controller offers superior performance and accuracy compared to previous methods.
Area Of Science
- Neuroscience
- Biomedical Engineering
- Control Systems
Background
- Asynchronous intrafascicular multi-electrode stimulation (aIFMS) enables fatigue-resistant, graded muscle forces.
- Previous controllers (MISO-δI, feedforward) had lagged responses and lacked immediate corrections.
- Limitations in prior methods hindered precise control for neuroprosthetics.
Purpose Of The Study
- Introduce an adaptive feedforward model predictive controller (aF-MPC) for isometric torque control.
- Enhance existing aIFMS feedforward control with predictive capabilities and online model learning.
- Address limitations of lagged responses and lack of immediate control corrections in aIFMS.
Main Methods
- Developed and evaluated the aF-MPC in anesthetized felines with sciatic nerve implants.
- Enhanced aIFMS feedforward control with a predictive policy and online model learning.
- Performed statistical and observational comparisons against F-MPC and MISO-δI controllers.
Main Results
- The aF-MPC demonstrated significant performance improvements over the non-adaptive F-MPC.
- Observationally, the aF-MPC outperformed the MISO-δI controller across all metrics.
- The aF-MPC accurately tracked desired torque profiles, even under high-frequency commands.
Conclusions
- The aF-MPC effectively manages unknown dynamics in aIFMS for superior isometric torque control.
- This adaptive controller surpasses previous methods in accuracy and responsiveness.
- aF-MPC with aIFMS presents a promising approach for developing naturalistic motor neuroprostheses.
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