Querying the user properly for high-performance brain-machine interfaces: Recursive estimation, control, and feedback information-theoretic perspectives

We propose a complementary approach to the design of neural prosthetic interfaces that goes beyond the standard approach of estimating desired control signals from neural activity. We exploit the fact that the for a user’s intended application, the dynamics of the prosthetic in fact impact subsequent desired control inputs. We illustrate that changing the dynamic re-sponse of a prosthetic device can make specific tasks signif-icantly easier to accomplish. Our approach relies upon prin-ciples from stochastic control and feedback information the-ory, and we illustrate its effectiveness both theoretically and experimentally- in terms of spelling words from a menu of characters using binary surface electromyography classifica-tion. Index Terms — neural prosthetics, feedback information theory, stochastic control, interface design 1

Approximate Steering of a Unicycle Under Bounded Model Perturbation Using Ensemble Control

This paper considers the problem of steering a nonholonomic unicycle despite model perturbation that scales both the forward speed and the turning rate by an unknown but bounded constant. We model the unicycle as an ensemble control system, show that this system is ensemble controllable, and derive an approximate steering algorithm that brings the unicycle to within an arbitrarily small neighborhood of any given Cartesian position. We apply our work to a differential-drive robot with unknown but bounded wheel radius and validate our approach with hardware experiments

A control theoretic approach to robotassisted locomotor therapy

Abstract-This paper proposes a control theoretic strategy for human walking gait assistance based on underactuated potential energy shaping. We design a simple control law that lessens the perceived weight of the patient's center of mass through a robotic ankle-foot orthosis (AFO) with one actuated degree-of-freedom. We then adopt a passive "compass-gait" bipedal walker as an implicit model of human locomotor behavior, which we simulate to draw beneficial implications for rehabilitation such as energy regulation, improved stability, and progressive training by Lyapunov funneling. Given current challenges in developing effective robot-assisted locomotor therapies, this paper offers a novel systematic approach to control strategy design for gait training and at-home assistance

Controlling Many Differential- Drive Robots with Uniform Control Inputs

This paper derives both open-loop and closed-loop control policies that steer a finite set of differential-drive robots to desired positions in a two-dimensional workspace, when all robots receive the same control inputs but each robot turns at a slightly different rate. In the absence of perturbation, the open-loop policy achieves zero error in finite time. In the presence of perturbation, the closed-loop policy is globally asymptotically stabilizing with state feedback. Both policies were validated with hardware experiments using up to 15 robots. These experimental results suggest that similar policies might be applied to control micro- and nanoscale robotic systems, which are often subject to similar constraints

Estimating System State During Human Walking with a Powered Ankle-Foot Orthosis

This paper presents a state estimator that reliably detects gait events during human walking with a portable powered ankle-foot orthosis (AFO), based only on measurements of the ankle angle and of contact forces at the toe and heel. Effective control of the AFO critically depends on detecting these gait events. A common approach detects gait events simply by checking if each measurement exceeds a given threshold. Our approach uses cross correlation between a window of past measurements and a learned model to estimate the configuration of the human walker, and detects gait events based on this estimate. We tested our approach in experiments with five healthy subjects and with one subject that had neuromuscular impairment. Using motion capture data for reference, we compared our approach to one based on thresholding and to another common one based on k -nearest neighbors. The results showed that our approach reduced the RMS error by up to 40% for the impaired subject and up to 49% for the healthy subjects. Moreover, our approach was robust to perturbations due to changes in walking speed and to control actuation