12 papers
An Adaptive-Sampling Control Framework for Constrained Linear Systems with Robust Safety Guarantees
Spencer Schutz, Charlott Vallon, Francesco Borrelli
Adaptive-sampling control balances control performance with resource efficiency. However, existing methods either fail to guarantee robust constraint satisfaction during rate trans…
Safe Adaptive-Sampling Control via Robust M-Step Hold Model Predictive Control
Spencer Schutz, Charlott Vallon, Francesco Borrelli
In adaptive-sampling control, the control frequency can be adjusted during task execution. Ensuring that these changes do not jeopardize the safety of the system being controlled r…
On Sampling Time and Invariance
Spencer Schutz, Charlott Vallon, Ben Recht +1
Invariant sets define regions of the state space where system constraints are always satisfied. The majority of numerical techniques for computing invariant sets have been develope…
Multi-Objective Learning Model Predictive Control
Siddharth H. Nair, Charlott Vallon, Francesco Borrelli
Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex cont…
Learning Hierarchical Control For Multi-Agent Capacity-Constrained Systems
Charlott Vallon, Alessandro Pinto, Bartolomeo Stellato +1
This paper introduces a novel data-driven hierarchical control scheme for managing a fleet of nonlinear, capacity-constrained autonomous agents in an iterative environment. We prop…
Learning Hierarchical Control Systems for Autonomous Systems with Energy Constraints
Charlott Vallon, Mark Pustilnik, Alessandro Pinto +1
This paper focuses on the design of hierarchical control architectures for autonomous systems with energy constraints. We focus on systems where energy storage limitations and slow…
Data-Driven Strategies for Hierarchical Predictive Control in Unknown Environments
Charlott Vallon, Francesco Borrelli
This article proposes a hierarchical learning architecture for safe data-driven control in unknown environments. We consider a constrained nonlinear dynamical system and assume the…
Learning to Satisfy Unknown Constraints in Iterative MPC
Monimoy Bujarbaruah, Charlott Vallon, Francesco Borrelli
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints. At each iteration of a repetitive task…
Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems
Charlott Vallon, Francesco Borrelli
A Task Decomposition method for iterative learning Model Predictive Control (TDMPC) for linear time-varying systems is presented. We consider the availability of state-input trajec…
Data-Driven Hierarchical Predictive Learning in Unknown Environments
Charlott Vallon, Francesco Borrelli
We propose a hierarchical learning architecture for predictive control in unknown environments. We consider a constrained nonlinear dynamical system and assume the availability of…
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint
Monimoy Bujarbaruah, Charlott Vallon
This paper proposes an Adaptive Stochastic Model Predictive Control (MPC) strategy for stable linear time-invariant systems in the presence of bounded disturbances. We consider mul…
Task Decomposition for Iterative Learning Model Predictive Control
Charlott Vallon, Francesco Borrelli
A task decomposition method for iterative learning model predictive control is presented. We consider a constrained nonlinear dynamical system and assume the availability of state-…