Online identification and nonlinear control of the electrically stimulated quadriceps muscle

A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under nonisometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shank-quadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is explicitly designed whereas the model was identified a priori using the developed identification procedure

Modelling dynamic decision making with the ACT-R cognitive architecture

This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition-the Dynamic Stocks and Flows Challenge-to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered

On the control of paraplegic standing using functional electrical stimulation

This thesis is concerned with the restoration of upright standing after spinal cord injury (SCI) by the means of Functional Electrical Stimulation. In particular, the work presented in this thesis is concerned with unsupported standing, i.e. standing without any support by the arms for stabilisation. Firstly, the experimental apparatus and feedback control approach is described. Secondly, the experimental work is divided into three parts. The motivation, experimental setup and procedure as well as results and conclusions are given for each of them. The feasibility of the investigated approach was usually tested on a neurologically intact subject. The results were subsequently confirmed with a paraplegic subject. First the feasibility and fundamental limitations of unsupported standing were investigated. Assuming the subject as a single-link inverted pendulum, an improved fully dynamic control approach was employed in the first step, confirming existing results. Here, the voluntary influence by the central nervous system was minimised. However, it is naturally desirable to take advantage of the residual sensory-motor abilities of the paraplegic subject to ease the task of stabilising the body. Ankle stiffness control has been proposed in the literature to accomplish this task. Hitherto, ankle stiffness was provided by artificial actuators. In the second part we investigated the feasibility and limitations of ankle stiffness control by means of FES. The same single-link approach was employed as above. Ankle stiffness control by FES was used in the third part to enable paraplegic standing. Here, the subject was required to participate actively in the task of stable standing and, while doing so, behaving like a double-link inverted pendulum. It could be shown that FES-controlled ankle stiffness contributed crucially to the subject's ability to stand. The thesis concludes with propositions for future work

Robust model predictive control for discrete-time fractional-order systems

In this paper we propose a tube-based robust model predictive control scheme for fractional-order discrete-time systems of the Grunwald-Letnikov type with state and input constraints. We first approximate the infinite-dimensional fractional-order system by a finite-dimensional linear system and we show that the actual dynamics can be approximated arbitrarily tight. We use the approximate dynamics to design a tube-based model predictive controller which endows to the controlled closed-loop system robust stability properties