Adaptive Feedforward Model Predictive Control for Torque Generation Through Asynchronous Intrafascicular Multi-Electrode Stimulation

Abstract

Objective: Fatigue-resistant and graded muscle forces can be evoked through asynchronous intrafascicular multi-electrode stimulation (aIFMS). Prior studies on controlled force generation using aIFMS employed either a feedback controller featuring a multiple-input single-output delayed-integral (MISO-$\delta$I) control law, or a feedforward controller with a non-predictive model-based policy. However, these controllers resulted in lagged responses as stimulation was coordinated via intentional time delays and lacked immediate control corrections. To address these limitations, this paper presents an adaptive feedforward model predictive controller (aF-MPC) for isometric torque control. Methods: The aF-MPC was evaluated in experiments in anesthetized felines implanted with Utah Slanted Electrode Arrays in their sciatic nerves. This controller redesigned the existing aIFMS feedforward controller by enhancing it with a predictive policy and an online model learning algorithm to compensate for unaccounted aIFMS effects. Statistical comparisons of the aF-MPC and the (non-adaptive) F-MPC trials and observational comparisons of the aF-MPC and the MISO-$\delta$I controller were performed for different desired trajectories. Results: The aF-MPC exhibited significant performance improvements over the F-MPC across multiple metrics. Observationally, the aF-MPC showed improvements in all performance metrics over the MISO-$\delta$I controller. Conclusion: Despite unknown dynamics in the aIFMS system, this paper's aF-MPC outperformed alternate approaches as it accurately tracked desired torque profiles even under high-frequency commands. Significance: The application of the aF-MPC in conjunction with aIFMS could provide a better avenue for developing naturalistic motor neuroprosthesis than F-MPCs or MISO-$\delta$I controllers.