Functional electrical stimulation (FES) involves artificial activation of paralyzed muscles via implanted electrodes. FES has been successfully used to improve the ability of tetraplegics to perform upper limb movements important for daily activities. The variety of movements that can be generated by FES is, however, limited to a few movements such as hand grasp and release. Ideally, a user of an FES system would have effortless command over all of the degrees of freedom associated with upper limb movement. One reason that a broader range of movements has not been implemented is because of the substantial challenge associated with identifying the patterns of muscle stimulation needed to elicit additional movements. The first part of this dissertation addresses this challenge by using a probabilistic algorithm to estimate the patterns of muscle activity associated with a wide range of upper limb movements.A neuroprosthetic involves the control of an external device via brain activity. Neuroprosthetics have been successfully used to improve the ability of tetraplegics to perform tasks important for interfacing with the world around them. The variety of mechanisms which they can control is, however, limited to a few devices such as special computer typing programs. Because motor areas of the cerebral cortex are known to represent and regulate voluntary arm movements it might be possible to sense this activity with electrodes and decipher this information in terms of a moment-by-moment representation of arm trajectory. Indeed, several methods for decoding neural activity have been described, but these approaches are encumbered by technical difficulties. The second part of this dissertation addresses this challenge by using similar probabilistic methods to extract arm trajectory information from electroencephalography (EEG) electrodes that are already chronically deployed and widely used in human subjects.Ultimately, the two approaches developed as part of this dissertation might serve as a flexible controller for interfacing brain activity with functional electrical stimulation systems to realize a brain-controlled upper-limb neuroprosthetic system capable of eliciting natural movements. Such a system would effectively bypass the injured region of the spinal cord and reanimate the arm, greatly increasing movement capability and independence in paralyzed individuals.

A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.