5 accepted papers
This paper presents a novel Koopman operator formulation for Euler–Lagrangian dynamics that employs an implicit generalized momentum-based state space representation, which decouples a known linear actuation channel from state-dependent dynamics and makes the system more amenable to linear Koopman m…
Due to dynamic variations such as changing payload, aerodynamic disturbances, and varying platforms, a robust solution for quadrotor trajectory tracking remains challenging. To address these challenges, we present a deep reinforcement learning (DRL) framework that achieves physical dynamics invarian
This letter proposes a combined Koopman-ZNN (Zeroing Neural Network) architecture for real-time control of redundant manipulators subject to input constraints. An autoencoder-based neural architecture is employed to discover the bilinear Koopman model for manipulator dynamics in joint space using in
Small-object detection and avoidance in unknown environments is a significant challenge to overcome for small autonomous vehicles that are generally highly agile and restricted in payload and computational processing power. Typical machine-vision and range measurement based solutions suffer either f…
Weakly electric fish use a navigational technique called electrolocation to investigate their surroundings for predator, prey and obstacles. Obstacles and other global stimuli are perceived as perturbations to the fish's self-generated electric field, which provide relevant navigational cues to the…