Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …

Recently, emerging technologies have assisted the healthcare system in the treatment of a
wide range of diseases so considerably that the development of such methods has been …

We present an algorithmic framework for stochastic model predictive control that is able to
optimize non-linear systems with cost functions that have sparse, discontinuous gradient …

We present a control method for improved repetitive path following for a ground vehicle that
is geared toward longterm operation, where the operating conditions can change over time …

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over
rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles …

Many critical robotics applications require robustness to disturbances arising from
unplanned forces, state uncertainty, and model errors. Motion planning algorithms that …

The control and operation of autonomous systems often involve different decision layers.
The higher control levels are responsible for the planning and perception and operate on a …

Researchers rely increasingly on tools from machine learning to improve the performance of
control algorithms on real world tasks and enable robots to operate for long periods of time …

This paper presents a robust-adaptive nonlinear model predictive control (MPC) technique
that leverages past experiences to achieve tractability on computationally constrained …

Overactuated omnidirectional flying vehicles are capable of generating force and torque in
any direction, which is important for applications such as contact-based industrial inspection …