Search Results for author:
Found 5 papers, 1 papers with code
Gain-Only Neural Operator Approximators of PDE Backstepping Controllers
For the recently introduced deep learning-powered approach to PDE backstepping control, we present an advancement applicable across all the results developed thus far: approximating the control gain function only (a function of one variable), rather than the entire kernel function of the backstepping transformation (a function of two variables).
Towards a MATLAB Toolbox to compute backstepping kernels using the power series method
Our first contribution is the development of initial steps towards a MATLAB toolbox dedicated to backstepping kernel computation.
MPC for Tracking applied to rendezvous with non-cooperative tumbling targets ensuring stability and feasibility
A Model Predictive Controller for Tracking is introduced for rendezvous with non-cooperative tumbling targets in active debris removal applications.
A hybrid dynamical system approach to the impulsive control of spacecraft rendezvous (extended version)
This paper introduces a hybrid dynamical system methodology for managing impulsive control in spacecraft rendezvous and proximity operations under the Hill-Clohessy-Wiltshire model.
Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation
The recently introduced neural operators (NO) can be trained to produce the gain functions, rapidly in real time, for each state value, without requiring a PDE solution.
