About

Andrew Teel is a Distinguished Professor in the Department of Electrical and Computer Engineering at UC Santa Barbara. His research interests focus on the development of feedback control algorithms for nonlinear and hybrid dynamical systems. He is associated with the Control and Computation Research Lab and is involved in advancing control theory and its applications. For contact, he can be reached by phone at +1 805-893-3616 or via email at teel@ece.ucsb.edu. His office is located in Harold Frank Hall, Room 5119A.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Engineering
• Machine Learning
• Control engineering
• Mathematical optimization
• Algorithm
• Pure mathematics
• Law
• Combinatorics

Selected publications

• Data-Driven Control of Continuous-Time LTI Systems via Non-Minimal Realizations

  IEEE Transactions on Automatic Control · 2026-01-01

  preprintOpen accessSenior author

  This article proposes an approach to design output- feedback controllers for unknown continuous-time linear time-invariant systems using only input-output data from a single experiment. To address the lack of state and derivative measurements, we introduce non-minimal realizations whose states can be observed by filtering the available data. We first apply this concept to the disturbance-free case, formulating linear matrix inequalities (LMIs) from batches of sampled signals to design a dynamic, filter-based stabilizing controller. The framework is then extended to the problem of asymptotic tracking and disturbance rejection—in short, output regulation—by incorporating an internal model based on prior knowledge of the disturbance/reference frequencies. Finally, we discuss tuning strategies for a class of multi-input multi-output systems and illustrate the method via numerical examples.

  PublisherDOI

• Hybrid Set-Seeking Systems: <b>Model-Free Feedback Optimization via Hybrid Inclusions</b>

  IEEE Control Systems · 2026-04-01 · 1 citations

  articleSenior author
  PublisherDOI

• Stochastic approximations of differential inclusions: almost sure boundedness and asymptotic convergence

  IFAC-PapersOnLine · 2025-01-01

  articleOpen access1st authorCorresponding

  For a stochastic approximation of a differential inclusion, results are given on almost sure boundedness of the approximate solutions and on their convergence to the chain recurrent part of the global attractor. The former involve bounding the stochastic approximation’s variance using a Lyapunov-like function. The novelty of the latter is in the use of Lyapunov-like characterizations of Morse decompositions of the attractor.

  PublisherDOI

• Robust Recurrence of Discrete-Time Infinite-Horizon Stochastic Optimal Control with Discounted Cost

  ArXiv.org · 2025-04-29

  preprintOpen accessSenior author

  We analyze the stability of general nonlinear discrete-time stochastic systems controlled by optimal inputs that minimize an infinite-horizon discounted cost. Under a novel stochastic formulation of cost-controllability and detectability assumptions inspired by the related literature on deterministic systems, we prove that uniform semi-global practical recurrence holds for the closed-loop system, where the adjustable parameter is the discount factor. Under additional continuity assumptions, we further prove that this property is robust.

  PublisherOA PDFDOI

• Derivative-free data-driven control of continuous-time linear time-invariant systems

  European Journal of Control · 2025-07-28 · 2 citations

  articleOpen accessSenior author

  This paper develops a method for data-driven stabilization of continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework is based on linear matrix inequalities (LMIs) and illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. In particular, we avoid differentiation by employing low-pass filters of the measured signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the approach is showcased via numerical examples.

  PublisherDOI

• Robust Recurrence of Discrete-Time Infinite-Horizon Stochastic Optimal Control with Discounted Cost

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  We analyze the stability of general nonlinear discrete-time stochastic systems controlled by optimal inputs that minimize an infinite-horizon discounted cost. Under a novel stochastic formulation of cost-controllability and detectability assumptions inspired by the related literature on deterministic systems, we prove that uniform semi-global practical recurrence holds for the closed-loop system, where the adjustable parameter is the discount factor. Under additional continuity assumptions, we further prove that this property is robust.

  PublisherDOI

• A smooth Conley–Lyapunov function for hybrid inclusions on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e23"><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">R</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:math>

  Systems & Control Letters · 2025-07-29

  articleSenior author
  PublisherDOI

• Emulation-based Stabilization for Networked Control Systems with Stochastic Channels

  arXiv (Cornell University) · 2024-01-22

  preprintOpen access

  This paper studies the stabilization problem of networked control systems (NCSs) with random packet dropouts caused by stochastic channels. To describe the effects of stochastic channels on the information transmission, the transmission times are assumed to be deterministic, whereas the packet transmission is assumed to be random. We first propose a stochastic scheduling protocol to model random packet dropouts, and address the properties of the proposed stochastic scheduling protocol. The proposed scheduling protocol provides a unified modelling framework for a general class of random packet dropouts due to different stochastic channels. Next, the proposed scheduling protocol is embedded into the closed-loop system, which leads to a stochastic hybrid model for NCSs with random packet dropouts. Based on this stochastic hybrid model, we follow the emulation approach to establish sufficient conditions to guarantee uniform global asymptotical stability in probability. In particular, an upper bound on the maximally allowable transmission interval is derived explicitly for all stochastic protocols satisfying Lyapunov conditions that guarantee uniform global asymptotic stability in probability. Finally, two numerical examples are presented to demonstrate the derived results.

  PublisherOA PDFDOI

• A Switched System Dwell-Time Update Mechanism for Path Following With Intermittent State Feedback Constraints

  IEEE Transactions on Automatic Control · 2024-02-06 · 1 citations

  article

  This article focuses on a class of problems where a switched system approach is used to model intermittencies in state feedback. Specifically, a single-agent with nonlinear dynamics is tasked with following a desired path that lies in a feedback-denied region where state feedback is unavailable. As a result, the agent must use open-loop state estimates (dead-reckon) to navigate along the desired path while periodically returning to a known feedback region where the accumulated dead-reckoning errors are regulated. A dwell-time update mechanism is developed that leverages intermittent data measurements to generate a relaxed dwell-time condition in comparison to previous literature. The updated dwell-time conditions are used to plan the duration spent following the desired path before returning to the feedback region to acquire state feedback. A Lyapunov-based switched system dwell-time analysis is used to show the state tracking error is uniformly ultimately bounded to a prescribed error threshold that also guarantees re-entry of the agent to the feedback region. Comparative numerical simulations are conducted to demonstrate the efficacy of the developed method.

  PublisherDOI

• Some converse Lyapunov-like results for strong forward invariance

  2024-12-16 · 3 citations

  articleSenior author

  In the setting of a differential inclusion, strong forward invariance of a closed or a compact set is studied. Main results are novel necessary Lyapunov-like conditions for this property. They involve time-varying and autonomous Lyapunov/barrier functions that are smooth everywhere or at least outside the invariant set and are decreasing or at least not increasing faster than exponentially.

  PublisherDOI

Recent grants

• NIH Grant R21AI057071

  NIH · $390k · 2006

• Uncertain Hybrid Systems

  NSF · $240k · 2006–2009

• Further advances in stability analysis for hybrid adversarial Markov decision processes

  NSF · $376k · 2012–2015

• Computational Sampled-Data Nonlinear Control

  NSF · $210k · 2003–2006

• Dynamical systems with random influences mixing logic and physics: a framework enabling control engineers to design for resilient autonomy

  NSF · $350k · 2015–2019

Frequent coauthors

• Dragan Nešić

  767 shared

• Warren E. Dixon

  University of Florida

  697 shared

• P Khargonekar

  Office of International Affairs

  688 shared

• Maria Prandini

  688 shared

• Marco Lovera

  Politecnico di Milano

  688 shared

• John Lygeros

  ETH Zurich

  688 shared

• Robert R. Bitmead

  University of California, San Diego

  688 shared

• James Farrell

  University of Arizona

  688 shared

Labs

• Andrew Teel's LabPI

  Not provided