25 citations · 67 across the 17 of their papers we have counts for
9 papers · 1 filter
Autonomous Navigation and Station-Keeping on Near-Rectilinear Halo Orbits
Yuri Shimane, Karl Berntorp, Stefano Di Cairano +1
This article develops an optical navigation (OPNAV) and station-keeping pipeline for the near-rectilinear halo orbit (NRHO) in high-fidelity ephemeris model dynamics, using synthet…
Data-Driven Motion Planning for Uncertain Nonlinear Systems
Babak Esmaeili, Hamidreza Modares, Stefano Di Cairano
This paper proposes a data-driven motion-planning framework for nonlinear systems that constructs a sequence of overlapping invariant polytopes. Around each randomly sampled waypoi…
Revolution-Spaced Output-Feedback Model Predictive Control for Station Keeping on Near-Rectilinear Halo Orbits
Yuri Shimane, Stefano Di Cairano, Koki Ho +1
We develop a model predictive control (MPC) policy for station keeping on a Near-Rectilinear Halo Orbit (NRHO). The proposed policy achieves full-state tracking of a reference NRHO…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Automated Controller Calibration by Kalman Filtering
Marcel Menner, Karl Berntorp, Stefano Di Cairano
This paper proposes a method for calibrating control parameters. Examples of such control parameters are gains of PID controllers, weights of a cost function for optimal control, f…
Path Planning using Positive Invariant Sets
Claus Danielson, Avishai Weiss, Karl Berntorp +1
We present an algorithm for steering the output of a linear system from a feasible initial condition to a desired target position, while satisfying input constraints and non-convex…
Indirect-adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty
Stefano Di Cairano
We develop an indirect-adaptive model predictive control algorithm for uncertain linear systems subject to constraints. The system is modeled as a polytopic linear parameter varyin…
Explicit model predictive control accuracy analysis
Andrew Knyazev, Peizhen Zhu, Stefano Di Cairano
Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be…
Extremum Seeking-based Iterative Learning Linear MPC
Mouhacine Benosman, Stefano Di Cairano, Avishai Weiss
In this work we study the problem of adaptive MPC for linear time-invariant uncertain models. We assume linear models with parametric uncertainties, and propose an iterative multi-…