Neuromorphic computing for attitude estimation onboard quadrotors

Onboard State Dependent LQR for Agile Quadrotors

State-of-the-art approaches in quadrotor control split the problem into multiple cascaded subproblems, exploiting the different time scales of the rotational and translational dynamics. They calculate a desired acceleration as input for a cascaded attitude controller but omit the attitude dynamics. These approaches use limits on the desired acceleration to maintain feasibility and robustness th...

Robust Attitude Control for Quadrotors with External Disturbances

This study investigates a design procedure for a robust nonlinear control algorithm based on sliding mode control (SMC) to stabilize the attitude of a 3-DOF quadrotor UAV subject to external disturbances. Since traditional sliding mode controllers are sensitive against external disturbances in the reaching phase, a new algorithm is proposed to enhance the robust performance of an SMC strategy. ...

Tutorial: Neuromorphic computing

ReRAM-Based Neuromorphic Computing

Artificial neural networks are currently being used as a promising means to solve problems in machine learning and computer science [1–3]. These networks are loosely inspired by massively parallel biological neural systems, but they are often implemented on mostly sequential hardware computing platforms, ignoring key features of real neural processing systems, such as their ability to carry out...

Automotive Computing, Neuromorphic Computing, and Beyond

Welcome to 2018 and IEEE Micro’s 38th year. I’m delighted about last year’s issues. In addition to the traditional special issues on Hot Chips and Top Picks, we had special issues on cognitive architectures, architectures for the post-Moore era, Cool Chips and Hot Interconnects, and ultra-low-power processors. I deeply appreciate the efforts of everyone who reviewed the many articles submitted ...