47 citations · 69 across the 5 of their papers we have counts for
7 papers
Hybrid Iterative Linear Quadratic Estimation: Optimal Estimation for Hybrid Systems
J. Joe Payne, James Zhu, Nathan J. Kong +1
In this paper we present Hybrid iterative Linear Quadratic Estimation (HiLQE), an optimization based offline state estimation algorithm for hybrid dynamical systems. We utilize the…
Saltation Matrices: The Essential Tool for Linearizing Hybrid Dynamical Systems
Nathan J. Kong, J. Joe Payne, James Zhu +1
Hybrid dynamical systems, i.e. systems that have both continuous and discrete states, are ubiquitous in engineering, but are difficult to work with due to their discontinuous trans…
Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots
Nathan J. Kong, Chuanzheng Li, Aaron M. Johnson
Model Predictive Control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC…
The Uncertainty Aware Salted Kalman Filter: State Estimation for Hybrid Systems with Uncertain Guards
J. Joe Payne, Nathan J. Kong, Aaron M. Johnson
In this paper we present a method for updating robotic state belief through contact with uncertain surfaces and apply this update to a Kalman filter for more accurate state estimat…
Hybrid Event Shaping to Stabilize Periodic Hybrid Orbits
James Zhu, Nathan J. Kong, George Council +1
Many controllers for legged robotic systems leverage open- or closed-loop control at discrete hybrid events to enhance stability. These controllers appear in several well studied p…
iLQR for Piecewise-Smooth Hybrid Dynamical Systems
Nathan J. Kong, George Council, Aaron M. Johnson
Trajectory optimization is a popular strategy for planning trajectories for robotic systems. However, many robotic tasks require changing contact conditions, which is difficult due…
The Salted Kalman Filter: Kalman Filtering on Hybrid Dynamical Systems
Nathan J. Kong, J. Joe Payne, George Council +1
Many state estimation and control algorithms require knowledge of how probability distributions propagate through dynamical systems. However, despite hybrid dynamical systems becom…