Automated Optimal Coordination of Multiple-DOF Neuromuscular Actions in Feedforward Neuroprostheses

Abstract

This paper describes a new method for designing feedforward controllers for multiple-muscle, multiple-degree-of-freedom, motor system neural prostheses. The design process is based on experimental measurement of the forward input-output properties of the neuromechanical system and numerical optimization of stimulation patterns to meet muscle coactivation criteria, thus resolving the muscle redundancy (i.e., over-control) and the coupled degrees of freedom problems inherent in neuromechanical systems. We designed feedforward controllers to control the isometric forces at the tip of the thumb in two directions during stimulation of three thumb muscles as a model system. We tested the method experimentally in ten able-bodied individuals and one patient with spinal cord injury. Good control of isometric force in both degrees of freedom was observed, with RMS errors less than 10% of the force range in seven experiments and statistically significant correlations between the actual and target forces in all ten experiments. Systematic bias and slope errors were observed in a few experiments, likely due to neuromuscular fatigue. Overall, the tests demonstrated the ability of a general design approach to satisfy both control and coactivation criteria in multiple muscle, multiple axis neuromechanical systems, and which is applicable to a wide range of neuromechanical systems and stimulation electrodes.

Predictor-Based Compensation for Electromechanical Delay During Neuromuscular Electrical Stimulation

“…The open-loop tracking error system can be developed by multiplying the time derivative of (9) by and utilizing the expressions in (1), (8), and (10) to obtain (12) where nonlinear functions and are defined as Let be defined as where the notation represents , expressed in terms of desired limb position and velocity. Based on (12) and the subsequent stability analysis, the voltage input is designed as (13) where is a known control gain that can be expanded as (14) to facilitate the subsequent analysis, where , , and are known constants. The closed-loop error system is determined by adding and subtracting to (12) and using (9) and (13) as (15) where the auxiliary terms are defined as (16) where and can be upper bounded as (17) In (17), is a known constant, the bounding function is a positive globally invertible nondecreasing function, and is defined as (18) where is defined as Based on (15) and the subsequent stability analysis, LK functionals are defined as (19) where is a known constant.…”

“…After adding and subtracting to (26), and utilizing (13), (14), (27), and (28), the following expression is obtained: (29) By completing the squares, the inequality in (29) can be upper bounded as (30) where is denoted as Since the expression in (30) can rewritten as (31) Using the definitions of in (18), in (22), and in (13), the expression in (31) can be upper bounded as (32) where is for some . By further utilizing (24), the inequality in (32) can be written as (33) Consider a set defined as (34) For , the linear differential equation in (33) can be solved as (35) provided the control gains and are selected according to the sufficient conditions in (21) (i.e., a semi-global result).…”

“…[2]- [14] and the references therein). Recently, efforts have been made to develop nonlinear control techniques [5], [15]- [19] to obtain improved error performance and/or asymptotic tracking [5], [17], [18] (proven through a closed-loop stability analysis), even in the presence of bounded exogenous disturbances [17], [18].…”

Closed-Loop Neural Network-Based NMES Control for Human Limb Tracking

“…[20]- [25] and the references within) in neural network (NN)-based NMES control development. One motivation for NN-based controllers is the desire to augment feedback methods with an adaptive element that can adjust to the uncertain muscle model, rather than only relying on feedback to dominate the uncertainty based on worse case scenarios.…”

A predictor-based compensation for electromechanical delay during neuromuscular electrical stimulation-II

“…[1]- [10] and the references therein) have been suggested for NMES control. Despite these efforts, several open challenges persist due to the presence of uncertainty, nonlinearity, muscle fatigue, unmodeled disturbances such as muscle spasticity or external changes in muscle loads, electromechanical delay (EMD), etc.…”