An intermittent predictive control approach to modelling sustained human motor control

Although human sustained control movements are continuous in nature there is still controversy on the mechanisms underlying such physiological systems. A popular topic of debate is whether human motor control mechanisms could be modelled as engineering control systems, and if so, what control algorithm is most appropriate. Since the early years of modelling sustained control tasks in human motor control the servomechanism has been an adequate model to describe human tracking tasks. Another continuous-time system model that is often used to model sustained control tasks is the predictive controller which is based on internal models and includes prediction and optimisation. On the other hand, studies have suggested intermittent behaviour of the ``human controller'' in sustained motor control tasks. This thesis investigated whether intermittent control is a suitable approach to describe sustained human motor control. It was investigated how well an intermittent control system model could approximate both the deterministic and non-deterministic parts of experimental data, from a visual-manual compensatory tracking task. Finally, a preliminary study was conducted to explore issues associated with the practical implementation of the intermittent control model. To fit the deterministic part of experimental data, a frequency domain identification method was used. Identification results obtained with an intermittent controller were compared against the results using continuous-time non-predictive and predictive controllers. The results show that the identified frequency response functions of the intermittent control model not only fit the frequency response functions derived from the experimental data well, but most importantly resulted in identified controller parameters which are similar to those identified using a predictive controller, and whose parameter values appear to be physiologically meaningful. A novel way to explain human variability, as represented by the non-deterministic part of the experimental data (the \emph{remnant}), was developed, based on an intermittent control model with variable intermittent interval. This model was compared against the established paradigm, in which variability is explained by a predictive controller with added noise, either signal dependent control signal noise, or observation noise. The study has shown that the intermittent controller with a variable intermittent interval could model the non-deterministic experimental data as well as the predictive controller model with added noise. This provides a new explanation for the source of remnant in human control as inherent to the controller structure, rather than as a noise signal, and enables a new interpretation for the physiological basis for human variability. Finally, the theoretical intermittent control model was implemented in real-time in the context of the physiological control mechanism of human standing balance. An experimental method was developed to apply automatic artificial balance of an inverted pendulum in the context of human standing, via functions electrical stimulation control of the lower leg muscles of a healthy subject. The significance of this study is, firstly, that frequency domain identification was applied for the first time with intermittent control, and it could be shown that both intermittent and predictive control models can model deterministic experimental data from manual tracking tasks equally well. Secondly, for the first time the inherent variability, which is represented by the remnant signal, in human motor control tasks could be modelled as part of the structure of the intermittent controller rather than as an added noise model. Although, the experimental method to apply automatic artificial balance of an inverted pendulum in the context of human standing was not successful, the intermittent controller was implemented for the first time in real-time and combined with electrical muscle stimulation to control a physiological mechanism.