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João P. Hespanha - IEEE Xplore Author Profile

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João P. Hespanha

Also published under: Joao Pedo Hespanha, Joao Pedro Hespanha, JoÃo P. Hespanha, J. Hespanha, J. P. Hespanha, João Hespanha, Joao P. Hespanha, J. M. Hespanha, Joo P. Hespanha, Joao Hespanha, João Pedro Hespanha, Joãao P. Hespanha

Publications

254

Citations

21,366

Publications by Year

19962024

Co-Authors:

A. Pedro AguiarT. AkinfievAmr AlanwarAndrea AlessandrettiMohd Shabbir Ali

Show All Co-Authors (218)

João P. Hespanha

Also published under: Joao Pedo Hespanha, Joao Pedro Hespanha, JoÃo P. Hespanha, J. Hespanha, J. P. Hespanha, João Hespanha, Joao P. Hespanha, J. M. Hespanha, Joo P. Hespanha, Joao Hespanha, João Pedro Hespanha, Joãao P. Hespanha

Affiliation

Dep. of Electrical and Computer Eng.

University of California

Santa Barbara, CA, USA

Publication Topics

Optimal Control,
Nonlinear Systems,
Cost Function,
Random Variables,
Control Input,
Dynamical,
Experimental Design,
Linear System,
Model Predictive Control,
Optimal Control Problem,
Stochastic Gradient Descent,
System Dynamics

Biography

João P. Hespanha (S'95–SM'02–F'08) received the Ph.D. degree in electrical engineering and applied science from Yale University, New Haven, CT, in 1998.,From 1999 to 2001, he was Assistant Professor at the University of Southern California, Los Angeles. He moved to the University of California, Santa Barbara in 2002, where he currently holds a Professor position with the Department of Electrical and Computer Engineering. He is Associate Director for the Center for Control, Dynamical-systems, and Computation (CCDC), Vice-Chair of the Department of Electrical and Computer Engineering, and a member of the Executive Committee for the Institute for Collaborative Biotechnologies (ICB). His current research interests include hybrid and switched sy...

Publications

254

Citations

21,366

Publications by Year

19962024

Co-Authors:

A. Pedro Aguiar
T. Akinfiev
Amr Alanwar
Andrea Alessandretti
Mohd Shabbir Ali

Show All Co-Authors (218)

Author's Published Works

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Showing 1-25 of 254 results

Conferences (203)

Journals (50)

Magazines (1)

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João P. Hespanha(113)
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Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA(53)
Center for Control, Dynamical Systems, and Computation, University of California, Santa Barbara, CA, USA(9)
University of California Santa Barbara, Santa Barbara, CA, US(7)
Center for Control Engineering and Computation, University of California, Santa Barbara, CA, USA(7)
Department of Electrical & Computer Engineering, University of California, Santa Barbara, CA, USA(6)
IEEE Transactions on Automatic Control(23)
2007 46th IEEE Conference on Decision and Control(8)
2007 American Control Conference(8)
Proceedings of the 44th IEEE Conference on Decision and Control(8)
2009 American Control Conference(7)
IEEE(254)
Media(1)
San Diego, CA, USA(15)
New Orleans, LA, USA(9)
Boston, MA, USA(8)
New York, NY, USA(8)
Seville, Spain(8)
Optimal Control(59)
Linear System(43)
Asymptotically Stable(29)
Nonlinear Systems(26)
Control Input(23)

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Results

Optimal Sampling Schedules for $h_{2}$ and $h_{\infty}$ State-Feedback Control

Duarte J. Antunes;João P. Hespanha

2024 European Control Conference (ECC)

Year: 2024 | Conference Paper |

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We consider a discrete-time linear system for which the control input is updated at every sampling time, but the state is measured at a slower rate. We allow the state to be sampled according to a periodic schedule, which dictates when the state should be sampled over a period. Given a desired average sampling interval, our goal is to determine sampling schedules that are optimal in the sense that...Show More

Optimal Sampling Schedules for $h_{2}$ and $h_{\infty}$ State-Feedback Control

Duarte J. Antunes;João P. Hespanha

2024 European Control Conference (ECC)

Year: 2024 | Conference Paper |

Control-Oriented Identification via Stochastic Optimization

Sean Anderson;João P. Hespanha

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

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Data-driven control benefits from rich datasets, but constructing such datasets becomes challenging when gathering data is limited. We consider an offline experiment design approach to gathering data where we design a control input to collect data that will most improve the performance of a feedback controller. We consider a setting in which the dynamics are modeled parametrically and formulate a ...Show More

Control-Oriented Identification via Stochastic Optimization

Sean Anderson;João P. Hespanha

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

Markov Chain Monte Carlo for Koopman-Based Optimal Control

João Hespanha;Kerem Çamsarı

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

Cited by: Papers (1)

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We propose a Markov Chain Monte Carlo (MCMC) algorithm based on Gibbs sampling with parallel tempering to solve nonlinear optimal control problems. The algorithm is applicable to nonlinear systems with dynamics that can be approximately represented by a finite dimensional Koopman model, potentially with high dimension. This algorithm exploits linearity of the Koopman representation to achieve sign...Show More

Markov Chain Monte Carlo for Koopman-Based Optimal Control

João Hespanha;Kerem Çamsarı

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

Learning Switched Koopman Models for Control of Entity-Based Systems

Madeline Blischke;João P. Hespanha

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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One problem that arises in control engineering is that of controlling a system for which the dynamics are unknown. Such a problem favors a data-driven approach, such as can be done through the use of the Koopman operator. We present a switched Koopman model, applicable to systems with discrete sets of inputs, that gives rise to an optimal control problem with a piecewise affine value function. Thi...Show More

Learning Switched Koopman Models for Control of Entity-Based Systems

Madeline Blischke;João P. Hespanha

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control

Sean Anderson;Katie Byl;João P. Hespanha

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Learning for control in repeated tasks allows for well-designed experiments to gather the most useful data. We consider the setting in which we use a data-driven controller that does not have access to the true system dynamics. Rather, the controller uses inferred dynamics based on the available information. In order to acquire data that is beneficial for this controller, we present an experimenta...Show More

Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control

Sean Anderson;Katie Byl;João P. Hespanha

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Convergent Second-Order Methods for Min-Max Optimizations

Raphael Chinchilla;Guosong Yang;João Hespanha

2023 American Control Conference (ACC)

Year: 2023 | Conference Paper |

We address the use of second-order methods to solve optimizations of the form\begin{equation*}\mathop {\min }\limits_{u \in \mathcal{U}} \mathop {\max }\limits_{d \in \mathcal{D}} f(u,d),\tag{1}\end{equation*}for a twice continuously differentiable function $f:\mathcal{U} \times \mathcal{D} \to \mathbb{R}$ and sets $\mathcal{U} \subset {\mathbb{R}^{{n_u}}},\mathcal{D} \subset {\mathbb{R}^{{n_d}}}$...Show More

Convergent Second-Order Methods for Min-Max Optimizations

Raphael Chinchilla;Guosong Yang;João Hespanha

2023 American Control Conference (ACC)

Year: 2023 | Conference Paper |

Game Theory for Autonomy: From Min-Max Optimization to Equilibrium and Bounded Rationality Learning

Kyriakos G. Vamvoudakis;Filippos Fotiadis;João P. Hespanha;Raphael Chinchilla;Guosong Yang;Mushuang Liu;Jeff S. Shamma;Lacra Pavel

2023 American Control Conference (ACC)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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Finding Nash equilibria in non-cooperative games can be, in general, an exceptionally challenging task. This is owed to various factors, including but not limited to the cost functions of the game being nonconvex/nonconcave, the players of the game having limited information about one another, or even due to issues of computational complexity. The present tutorial draws motivation from this harsh ...Show More

Game Theory for Autonomy: From Min-Max Optimization to Equilibrium and Bounded Rationality Learning

Kyriakos G. Vamvoudakis;Filippos Fotiadis;João P. Hespanha;Raphael Chinchilla;Guosong Yang;Mushuang Liu;Jeff S. Shamma;Lacra Pavel

2023 American Control Conference (ACC)

Year: 2023 | Conference Paper |

Biologically plausible robust control with Neural Network weight reset for unmanned aircraft systems under impulsive disturbances

Ignacio Rubio Scola;Luis Rodolfo Garcia Carrillo;Andrew T. Sornborger;João P. Hespanha

2023 American Control Conference (ACC)

Year: 2023 | Conference Paper |

Cited by: Papers (4)

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Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying stability analyses and mathematical proofs rely on unrealistic assumptions which limit not only the performance, but also the implementation of such controllers in real-world scenarios....Show More

Biologically plausible robust control with Neural Network weight reset for unmanned aircraft systems under impulsive disturbances

Ignacio Rubio Scola;Luis Rodolfo Garcia Carrillo;Andrew T. Sornborger;João P. Hespanha

2023 American Control Conference (ACC)

Year: 2023 | Conference Paper |

Memory Saving State-Sharing Multi-Observer for a Class of Multi-Observer-Based Algorithms

Michelle S. Chong;Masashi Wakaiki;João P. Hespanha

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

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A multi-observer is a bank of observers which is used for state estimation in various applications. However, it has an implementation bottleneck when a large number of observers are required for the desired estimation performance. To overcome this problem, we propose the design method of a state-sharing multi-observer for a class of nonlinear systems. The state-sharing multi-observer is a single o...Show More

Memory Saving State-Sharing Multi-Observer for a Class of Multi-Observer-Based Algorithms

Michelle S. Chong;Masashi Wakaiki;João P. Hespanha

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

Trajectories for the Optimal Collection of Information

Matthew R. Kirchner;David Grimsman;João P. Hespanha;Jason R. Marden

2023 IEEE Aerospace Conference

Year: 2023 | Conference Paper |

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We study a scenario where an aircraft has multiple heterogeneous sensors collecting measurements to track a target vehicle of unknown location. The measurements are sampled along the flight path and our goals to optimize sensor placement to minimize estimation error. We select as a metric the Fisher Information Matrix (FIM), as “minimizing” the inverse of the FIM is required to achieve small estim...Show More

Trajectories for the Optimal Collection of Information

Matthew R. Kirchner;David Grimsman;João P. Hespanha;Jason R. Marden

2023 IEEE Aerospace Conference

Year: 2023 | Conference Paper |

The Impact of Measurement Passing in Sensor Network Measurement Selection

David Grimsman;Matthew R. Kirchner;João P. Hespanha;Jason R. Marden

IEEE Transactions on Control of Network Systems

Year: 2023 | Volume: 10, Issue: 1 | Journal Article |

Cited by: Papers (1)

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This article considers a set of sensors, which, as a group, are tasked with taking measurements of the environment and sending a small subset of the measurements to a centralized data fusion center, where the measurements will be used to estimate the overall state of the environment. The sensors’ goal is to send the most informative set of measurements so that the estimate is as accurate as possib...Show More

The Impact of Measurement Passing in Sensor Network Measurement Selection

David Grimsman;Matthew R. Kirchner;João P. Hespanha;Jason R. Marden

IEEE Transactions on Control of Network Systems

Year: 2023 | Volume: 10, Issue: 1 | Journal Article |

Stochastic Programming Using Expected Value Bounds

Raphael Chinchilla;João P. Hespanha

IEEE Transactions on Automatic Control

Year: 2023 | Volume: 68, Issue: 6 | Journal Article |

Cited by: Papers (2)

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In this article, we address the problem of minimizing an expected value with stochastic constraints, known in the literature as stochastic programming. Our approach is based on computing and optimizing bounds for the expected value that are obtained by solving a deterministic optimization problem that uses the probability density function (pdf) to penalize unlikely values for the random variables....Show More

Stochastic Programming Using Expected Value Bounds

Raphael Chinchilla;João P. Hespanha

IEEE Transactions on Automatic Control

Year: 2023 | Volume: 68, Issue: 6 | Journal Article |

Limbic System-Inspired Performance-Guaranteed Control for Nonlinear Multi-Agent Systems With Uncertainties

Ignacio Rubio Scola;Luis Rodolfo Garcia Carrillo;João P. Hespanha

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 8 | Journal Article |

Cited by: Papers (6)

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We introduce a performance-guaranteed limbic system-inspired control (LISIC) strategy for nonlinear multi-agent systems (MASs) with uncertain high-order dynamics and external perturbations, where each agent in the MAS incorporates a LISIC structure to support the consensus controller. This novel approach, which we call double integrator LISIC (DILISIC), is designed to imitate double integrator dyn...Show More

Limbic System-Inspired Performance-Guaranteed Control for Nonlinear Multi-Agent Systems With Uncertainties

Ignacio Rubio Scola;Luis Rodolfo Garcia Carrillo;João P. Hespanha

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 8 | Journal Article |

State Estimation for Asynchronously Switched Sampled-Data Systems

Sharad C. Shankar;Guosong Yang;João P. Hespanha

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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Asynchronously switched sampled-data systems can help model power systems and vehicles that evolve in continuous-time with switching behavior and discrete time measurements. We address the problem of jointly estimating a switching signal, with uncertainty in the exact switching times, as well as the continuous states of the system. We prove stability of the standard Kalman Filter under uncertainty...Show More

State Estimation for Asynchronously Switched Sampled-Data Systems

Sharad C. Shankar;Guosong Yang;João P. Hespanha

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

Global Asymptotic Stability of Nonlinear Systems while Exploiting Properties of Uncertified Feedback Controllers via Opportunistic Switching

Paul K. Wintz;Ricardo G. Sanfelice;João P. Hespanha

2022 American Control Conference (ACC)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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A hybrid control strategy is introduced that renders a compact set uniformly globally asymptotically stable for a continuous-time plant by switching between a Lyapunov-certified feedback controller and an uncertified controller. This control strategy allows for the opportunistic use of a controller that has desirable performance but lacks a Lyapunov certificate. A pair of tunable threshold functio...Show More

Global Asymptotic Stability of Nonlinear Systems while Exploiting Properties of Uncertified Feedback Controllers via Opportunistic Switching

Paul K. Wintz;Ricardo G. Sanfelice;João P. Hespanha

2022 American Control Conference (ACC)

Year: 2022 | Conference Paper |

Fast Desynchronization Algorithms for Decentralized Medium Access Control Based on Iterative Linear Equation Solvers

Daniel Silvestre;Joao Hespanha;Carlos Silvestre

IEEE Transactions on Automatic Control

Year: 2022 | Volume: 67, Issue: 11 | Journal Article |

Cited by: Papers (2)

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We tackle the problem of having multiple transmitters cooperating to be desynchronized using a distributed algorithm. Although this problem can also be found in surveillance, it has the most impact in achieving a fair access to a wireless shared communication medium at the medium access control layer in the context of wireless sensor networks. In this article, we first theoretically investigate th...Show More

Fast Desynchronization Algorithms for Decentralized Medium Access Control Based on Iterative Linear Equation Solvers

Daniel Silvestre;Joao Hespanha;Carlos Silvestre

IEEE Transactions on Automatic Control

Year: 2022 | Volume: 67, Issue: 11 | Journal Article |

Stochastic and Deterministic State-Dependent Social Networks

Daniel Silvestre;Paulo Rosa;João P. Hespanha;Carlos Silvestre

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2022 | Volume: 52, Issue: 2 | Journal Article |

Cited by: Papers (6)

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This article investigates a political party or an association social network where members share a common set of beliefs. In modeling it as a distributed iterative algorithm with network dynamics mimicking the interactions between people, the problem of interest becomes that of determining: 1) the conditions when convergence happens in finite time and 2) the corresponding steady-state opinion. For...Show More

Stochastic and Deterministic State-Dependent Social Networks

Daniel Silvestre;Paulo Rosa;João P. Hespanha;Carlos Silvestre

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2022 | Volume: 52, Issue: 2 | Journal Article |

A Tale of Two Doses: Model Identification and Optimal Vaccination for COVID-19

Raphael Chinchilla;Guosong Yang;Murat K. Erdal;Ramon R. Costa;João P. Hespanha

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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We address the model identification and the computation of optimal vaccination policies for the coronavirus disease 2019 (COVID-19). We consider a stochastic Susceptible– Infected–Removed (SIR) model that captures the effect of multiple vaccine treatments, each requiring a different number of doses and providing different levels of protection against the disease. We show that the inclusion of vacc...Show More

A Tale of Two Doses: Model Identification and Optimal Vaccination for COVID-19

Raphael Chinchilla;Guosong Yang;Murat K. Erdal;Ramon R. Costa;João P. Hespanha

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Optimal experiment design with applications to Pharmacokinetic modeling

Murat K. Erdal;Kevin W. Plaxco;Julian Gerson;Tod E. Kippin;João P. Hespanha

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

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We study the problem of designing an input to a dynamical system that is optimal at estimating unknown parameters in the system’s model. We take the A and D optimality criteria on the Fisher Information Matrix associated with the estimation problem as our optimization objective. Our main motivation is the estimation of the physiological parameters that appear in pharmacokinetic dynamics using a re...Show More

Optimal experiment design with applications to Pharmacokinetic modeling

Murat K. Erdal;Kevin W. Plaxco;Julian Gerson;Tod E. Kippin;João P. Hespanha

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Game Balancing using Koopman-based Learning

Allan M. Avila;Maria Fonoberova;João P. Hespanha;Igor Mezić;Daniel Clymer;Jonathan Goldstein;Marco A. Pravia;Daniel Javorsek

2021 American Control Conference (ACC)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

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This paper addresses the analysis of how the outcome of a zero-sum two-player game is affected by the value of numerical parameters that are part of the game rules and/or winning criterion. This analysis aims at selecting numerical values for such parameters that lead to games that are “fair” or “balanced” in spite of the fact that the two players may have distinct attributes/capabilities. Motivat...Show More

Game Balancing using Koopman-based Learning

Allan M. Avila;Maria Fonoberova;João P. Hespanha;Igor Mezić;Daniel Clymer;Jonathan Goldstein;Marco A. Pravia;Daniel Javorsek

2021 American Control Conference (ACC)

Year: 2021 | Conference Paper |

A Robust Control Strategy With Perturbation Estimation for the Parrot Mambo Platform

Ignacio Rubio Scola;Gabriel Alexis Guijarro Reyes;Luis Rodolfo Garcia Carrillo;João Pedro Hespanha;Laurent Burlion

IEEE Transactions on Control Systems Technology

Year: 2021 | Volume: 29, Issue: 4 | Journal Article |

Cited by: Papers (13)

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This article addresses theoretical and practical challenges associated with a commercially available and ready-to-fly small-scale unmanned aircraft system (UAS) developed by Parrot SA: the Mambo quad rotorcraft. The dynamic model and the structure of the controller running onboard the UAS autopilot are not disclosed by its manufacturers. For this reason, a novel robust controller for discrete-time...Show More

A Robust Control Strategy With Perturbation Estimation for the Parrot Mambo Platform

Ignacio Rubio Scola;Gabriel Alexis Guijarro Reyes;Luis Rodolfo Garcia Carrillo;João Pedro Hespanha;Laurent Burlion

IEEE Transactions on Control Systems Technology

Year: 2021 | Volume: 29, Issue: 4 | Journal Article |

Resilient Desynchronization for Decentralized Medium Access Control

Daniel Silvestre;João P. Hespanha;Carlos Silvestre

IEEE Control Systems Letters

Year: 2021 | Volume: 5, Issue: 3 | Journal Article |

Cited by: Papers (9)

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In Wireless Sensor Networks (WSNs), equally spaced timing for Medium Access Control (MAC) is fundamental to guarantee throughput maximization from all nodes. This motivated the so called desynchronization problem and its solution based on the fast Nesterov method. In this letter, we tackle the problem of constructing centralized and distributed versions of the optimal fixed-parameter Nesterov that...Show More

Resilient Desynchronization for Decentralized Medium Access Control

Daniel Silvestre;João P. Hespanha;Carlos Silvestre

IEEE Control Systems Letters

Year: 2021 | Volume: 5, Issue: 3 | Journal Article |

A Hamilton–Jacobi Formulation for Optimal Coordination of Heterogeneous Multiple Vehicle Systems

Matthew R. Kirchner;Mark J. Debord;João P. Hespanha

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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We present a method for optimal coordination of multiple vehicle teams when multiple endpoint configurations are equally desirable, such as seen in the autonomous assembly of formation flight. The individual vehicles’ positions in the formation are not assigned a priori and a key challenge is to find the optimal configuration assignment along with the optimal control and trajectory. Commonly, assi...Show More

A Hamilton–Jacobi Formulation for Optimal Coordination of Heterogeneous Multiple Vehicle Systems

Matthew R. Kirchner;Mark J. Debord;João P. Hespanha

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

The Impact of Message Passing in Agent-Based Submodular Maximization

David Grimsman;Matthew R. Kirchner;João P. Hespanha;Jason R. Marden

2020 59th IEEE Conference on Decision and Control (CDC)

Year: 2020 | Conference Paper |

Cited by: Papers (4)

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Submodular maximization problems are a relevant model set for many real-world applications. Since these problems are generally NP-Hard, many methods have been developed to approximate the optimal solution in polynomial time. One such approach uses an agent-based greedy algorithm, where the goal is for each agent to choose an action from its action set such that the union of all actions chosen is a...Show More

The Impact of Message Passing in Agent-Based Submodular Maximization

David Grimsman;Matthew R. Kirchner;João P. Hespanha;Jason R. Marden

2020 59th IEEE Conference on Decision and Control (CDC)

Year: 2020 | Conference Paper |

A secure state estimation algorithm for nonlinear systems under sensor attacks

Michelle S. Chong;Henrik Sandberg;João P. Hespanha

2020 59th IEEE Conference on Decision and Control (CDC)

Year: 2020 | Conference Paper |

Cited by: Papers (10)

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The state estimation of continuous-time nonlinear systems in which a subset of sensor outputs can be maliciously controlled through injecting a potentially unbounded additive signal is considered in this paper. Analogous to our earlier work for continuous-time linear systems in [1], we term the convergence of the estimates to the true states in the presence of sensor attacks as `observability unde...Show More

A secure state estimation algorithm for nonlinear systems under sensor attacks

Michelle S. Chong;Henrik Sandberg;João P. Hespanha

2020 59th IEEE Conference on Decision and Control (CDC)

Year: 2020 | Conference Paper |

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