Riccardo Bonalli

Riccardo Bonalli

Stanford University

H-index: 12

North America-United States

About Riccardo Bonalli

Riccardo Bonalli, With an exceptional h-index of 12 and a recent h-index of 12 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Learning for Control, Risk-Averse Stochastic Optimal Control, Geometric Optimal Control, Numerical Methods for Optimal Control.

His recent articles reflect a diverse array of research interests and contributions to the field:

Mean-Covariance Steering of a Linear Stochastic System with Input Delay and Additive Noise

Estimating the convex hull of the image of a set with smooth boundary: error bounds and applications

Exact Characterization of the Convex Hulls of Reachable Sets

Risk-Averse Trajectory Optimization via Sample Average Approximation

Characterization of Singular Arcs in Spacecraft Trajectory Optimization

First-order Pontryagin maximum principle for risk-averse stochastic optimal control problems

A Gradient Descent-Ascent Method for Continuous-Time Risk-Averse Optimal Control

Non-Parametric Learning of Stochastic Differential Equations with Fast Rates of Convergence

Riccardo Bonalli Information

University

Position

___

Citations(all)

542

Citations(since 2020)

537

Cited By

85

hIndex(all)

12

hIndex(since 2020)

12

i10Index(all)

15

i10Index(since 2020)

15

Email

University Profile Page

Google Scholar

Riccardo Bonalli Skills & Research Interests

Learning for Control

Risk-Averse Stochastic Optimal Control

Geometric Optimal Control

Numerical Methods for Optimal Control

Top articles of Riccardo Bonalli

Mean-Covariance Steering of a Linear Stochastic System with Input Delay and Additive Noise

2024/1/9

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

Estimating the convex hull of the image of a set with smooth boundary: error bounds and applications

arXiv preprint arXiv:2302.13970

2023/2/27

Exact Characterization of the Convex Hulls of Reachable Sets

2023/12/13

Risk-Averse Trajectory Optimization via Sample Average Approximation

IEEE Robotics and Automation Letters

2023/11/10

Characterization of Singular Arcs in Spacecraft Trajectory Optimization

arXiv preprint arXiv:2311.04123

2023/11/7

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

Francesco Topputo
Francesco Topputo

H-Index: 22

First-order Pontryagin maximum principle for risk-averse stochastic optimal control problems

SIAM Journal on Control and Optimization

2023/6/30

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

A Gradient Descent-Ascent Method for Continuous-Time Risk-Averse Optimal Control

arXiv preprint arXiv:2306.12878

2023/6/22

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

Non-Parametric Learning of Stochastic Differential Equations with Fast Rates of Convergence

arXiv preprint arXiv:2305.15557

2023/5/24

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

Statistical Linearization for Robust Motion Planning

arXiv preprint arXiv:2303.01288

2023/3/2

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

Using spectral submanifolds for nonlinear periodic control

2022/12/6

2022 Index IEEE Control Systems Vol. 42

IEEE CONTROL SYSTEMS

2022/12

Convex Trajectory Planning [About This Issue]

IEEE Control Systems Magazine

2022/9/28

Convex optimization for trajectory generation: A tutorial on generating dynamically feasible trajectories reliably and efficiently

2022/9/28

Analysis of theoretical and numerical properties of sequential convex programming for continuous-time optimal control

IEEE Transactions on Automatic Control

2022/9/19

On the accessibility and controllability of statistical linearization for stochastic control: Algebraic rank conditions and their genericity

arXiv preprint arXiv:2207.10944

2022/7/22

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

Sample average approximation for stochastic programming with equality constraints

arXiv preprint arXiv:2206.09963

2022/6/20

A simple and efficient sampling-based algorithm for general reachability analysis

2022/5/11

Sequential convex programming for non-linear stochastic optimal control

ESAIM: Control, Optimisation and Calculus of Variations

2022

Risk-sensitive safety analysis using Conditional Value-at-Risk

IEEE Transactions on Automatic Control

2021/11/26

On the Convergence of Sequential Convex Programming for Non-Linear Optimal Control

PGMO DAYS 2021

2021/11/16

Riccardo Bonalli
Riccardo Bonalli

H-Index: 5

See List of Professors in Riccardo Bonalli University(Stanford University)

Co-Authors

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