17 citations · 24 across the 6 of their papers we have counts for
8 papers
MPC-based motion planning for non-holonomic systems in non-convex domains
Matthias Lorenzen, Teodoro Alamo, Martina Mammarella +1
Motivated by the application of using model predictive control (MPC) for motion planning of autonomous mobile robots, a form of output tracking MPC for non-holonomic systems and wi…
Multi-Waypoint Path Planning and Motion Control for Non-holonomic Mobile Robots in Agricultural Applications
Mahmoud Ghorab, Matthias Lorenzen
There is a growing demand for autonomous mobile robots capable of navigating unstructured agricultural environments. Tasks such as weed control in meadows require efficient path pl…
Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION
Martina Mammarella, Victor Mirasierra, Matthias Lorenzen +2
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of…
Computationally efficient stochastic MPC: a probabilistic scaling approach
Martina Mammarella, Teodoro Alamo, Fabrizio Dabbene +1
In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, whic…
An Offline-Sampling SMPC Framework with Application to Automated Space Maneuvers
Martina Mammarella, Matthias Lorenzen, Elisa Capello +5
In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive dis…
Stochastic MPC with Offline Uncertainty Sampling
Matthias Lorenzen, Fabrizio Dabbene, Roberto Tempo +1
For discrete-time linear systems subject to parametric uncertainty described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike…
Constraint-Tightening and Stability in Stochastic Model Predictive Control
Matthias Lorenzen, Fabrizio Dabbene, Roberto Tempo +1
Constraint tightening to non-conservatively guarantee recursive feasibility and stability in Stochastic Model Predictive Control is addressed. Stability and feasibility requirement…
An Improved Constraint-Tightening Approach for Stochastic MPC
Matthias Lorenzen, Frank Allgöwer, Fabrizio Dabbene +1
The problem of achieving a good trade-off in Stochastic Model Predictive Control between the competing goals of improving the average performance and reducing conservativeness, whi…