323 citations · 407 across the 32 of their papers we have counts for
13 papers · 1 filter
Unified Multi-Rate Control: from Low Level Actuation to High Level Planning
Ugo Rosolia, Andrew Singletary, Aaron D. Ames
In this paper we present a hierarchical multi-rate control architecture for nonlinear autonomous systems operating in partially observable environments. Control objectives are expr…
Constrained Risk-Averse Markov Decision Processes
Mohamadreza Ahmadi, Ugo Rosolia, Michel D. Ingham +2
We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a L…
Reactive motion planning with probabilistic safety guarantees
Yuxiao Chen, Ugo Rosolia, Chuchu Fan +2
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots. This paper considers th…
Decentralized Task and Path Planning for Multi-Robot Systems
Yuxiao Chen, Ugo Rosolia, Aaron D. Ames
We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) frame…
On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller
Ugo Rosolia, Yingzhao Lian, Emilio T. Maddalena +2
In this technical note we analyse the performance improvement and optimality properties of the Learning Model Predictive Control (LMPC) strategy for linear deterministic systems. T…
Formal Verification of Safety Critical Autonomous Systems via Bayesian Optimization
Prithvi Akella, Ugo Rosolia, Andrew Singletary +1
As control systems become increasingly more complex, there exists a pressing need to find systematic ways of verifying them. To address this concern, there has been significant wor…
Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach
Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R Stürz +2
We propose a novel approach to design a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertain system is modeled as linear parameter varyi…
Distributed Learning Model Predictive Control for Linear Systems
Yvonne R. Stürz, Edward L. Zhu, Ugo Rosolia +2
This paper presents a distributed learning model predictive control (DLMPC) scheme for distributed linear time invariant systems with coupled dynamics and state constraints. The pr…
Output-Lifted Learning Model Predictive Control
Siddharth H. Nair, Ugo Rosolia, Francesco Borrelli
We propose a computationally efficient Learning Model Predictive Control (LMPC) scheme for constrained optimal control of a class of nonlinear systems where the state and input can…
Control of Unknown Nonlinear Systems with Linear Time-Varying MPC
Dimitris Papadimitriou, Ugo Rosolia, Francesco Borrelli
We present a Model Predictive Control (MPC) strategy for unknown input-affine nonlinear dynamical systems. A non-parametric method is used to estimate the nonlinear dynamics from o…
Multi-Rate Control Design Leveraging Control Barrier Functions and Model Predictive Control Policies
Ugo Rosolia, Aaron D. Ames
In this paper we present a multi-rate control architecture for safety critical systems. We consider a high level planner and a low level controller which operate at different frequ…
Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
Edward L. Zhu, Yvonne R. Stürz, Ugo Rosolia +1
We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coup…
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions
Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia +3
Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performanc…