Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

This article provides a historical perspective of the field of adaptive control over the past
seven decades and its intersection with learning. A chronology of key events over this large …

The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

Considering the number of applications, and the quantity of research conducted over the
past few decades, it would not be an overstatement to label the piezoelectric materials as …

Heating, Ventilating, and Air Conditioning (HVAC) systems are the major energy-consuming
devices in buildings. Nowadays, due to the high demand for HVAC system installation in …

Adaptive control is a control methodology capable of dealing with uncertain systems to
ensure desired control performance. This paper provides an overview of some fundamental …

This paper provides a review of model predictive control (MPC) methods with active
uncertainty learning. System uncertainty poses a key theoretical and practical challenge in …

This work exposes a largely unexplored vector of physical-layer attacks with demonstrated
consequences in automobiles. By modifying the physical environment around analog …

Embedded sensing systems are pervasively used in life-and security-critical systems such
as those found in airplanes, automobiles, and healthcare. Traditional security mechanisms …

This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …