About

Dusan M. Stipanovic is a Professor in the Department of Industrial & Enterprise Systems Engineering at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Electrical Engineering with Distinction from Santa Clara University, where his dissertation focused on the stability and stabilization of nonlinear discontinuous systems. His educational background also includes a Master of Science in Electrical Engineering from Santa Clara University and a Diploma in Electrical Engineering from the University of Belgrade, Yugoslavia. His research areas encompass decision and control systems, with specific topics including distributed control of robot swarms, collision avoidance, multi-agent systems, and coverage control. He has contributed to chapters in books and numerous articles in journals, addressing issues such as cooperative pursuit, multi-robot coordination, and the stability of complex systems. His work emphasizes the application of Lyapunov-like barrier functions, stochastic optimal control, and decentralized algorithms to enhance autonomous system safety, efficiency, and robustness.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Mathematical optimization
• Machine Learning
• Algorithm
• Statistics
• Geometry
• Engineering
• Mathematical analysis

Selected publications

• Adaptive Parameter Avoidance Control and Safety-Corrected Tracking Framework for Multi-Agent Differential Drive Vehicles

  Actuators · 2026-04-20

  articleOpen accessCorresponding

  This paper presents a closed-form tracking and collision avoidance framework for multi-agent differential drive robots. Existing reactive methods often rely on purely geometric proximity, leading to conservative detours and local minima. A state-dependent adaptive avoidance strategy is developed to dynamically modulate repulsive forces using the time-derivative of fractional barrier risk functions, alleviating unnecessary evasive maneuvers. Within a convergence vector field (CVF) architecture, an active safety-corrected tracking mechanism orthogonally strips hazardous velocity projections from the spatial error. This mitigates the inherent conflict between target tracking and obstacle repulsion. A matrix projection-based Lyapunov approach demonstrates the finite-time convergence of the vehicle orientation, bounded tracking errors, and collision-free properties of the closed-loop system, with effectiveness further validated through simulations.

  PublisherOA PDFDOI

• Difference Equations and Machine Learning

  Synthesis lectures on mathematics and statistics · 2025-01-01

  book1st authorCorresponding
  PublisherDOI

• Variable Structure Cooperative Avoidance Control for Multiple Vehicles in a 3-D Space

  IEEE Transactions on Aerospace and Electronic Systems · 2025-10-06

  articleOpen access

  This paper presents a variable structure control approach for collision avoidance and trajectory tracking of multiple vehicles in 3D obstacle environments. Instead of relying only on barrier or avoidance functions, this method utilizes a sliding surface derived from the time derivative of a collision risk assessment function to trigger the avoidance control. Under this control framework, vehicles encountering avoidance conflicts smoothly converge toward the predefined sliding surface, achieving a collision avoidance strategy that balances conservatism and aggressiveness without compromising system safety. Auxiliary trajectories with avoidance information feedback are introduced to guide the vehicles, which autonomously facilitate the coordination between tracking and avoidance, eliminating the need for prioritization. This paper provides theoretical guarantees for the convergence and safety of the system through Lyapunov analysis. Two simulations, one involving multiple vehicles with obstacles and the other with dense obstacles, are conducted to showcase the effectiveness and reliability of the approach.

  PublisherDOI

• Reachability of a Soil Phosphorus Target That Satisfies Agricultural Production and Water Quality Goals

  Water Resources Research · 2025-03-01

  articleOpen accessSenior author

  Abstract Phosphorus fertilization has supported remarkable improvements in agricultural productivity but also degraded water quality. Watershed simulation models have been broadly instrumental to crafting phosphorus management responses. However, simulation‐based studies rely on predesigned watershed scenarios (e.g., initial conditions and management actions) and are blind to outcomes that might only emerge from unseen scenarios. Meanwhile, efforts to restore water quality have routinely failed. In contrast to simulation‐based methods, here we implement optimal control and reachability methods that describe watershed phosphorus trajectories for any initial condition and fertilizer strategy. The trade‐off is that these new methods require simplification of the system's dynamics. For a two‐pool phosphorus model, we define a dual management target where (a) plant‐available phosphorus satisfies crop demand but (b) total phosphorus losses meet water quality goals. From this target, we compute backwards‐reachable sets that indicate the minimum time in which the target can be reached from all initial conditions. For a typical watershed in the U.S. corn belt, we find that it will take at least 42 years to reach the joint agricultural and water quality target. We show that the optimal (time‐minimizing) fertilizer rate strategy drives a roundabout trajectory toward the target where soil phosphorus violates the crop demand threshold during the interim time. However, we find that even small, short‐term agricultural sacrifices can profoundly hasten progress toward the long‐term, joint target of agricultural productivity and water quality. These results and methods complement traditional simulation‐based studies and provide watershed managers with a richer characterization of uncertainty and management options.

  PublisherOA PDFDOI

• Dual-Region-Based Variable-Structure Avoidance Control Strategy for Multiple Car-Like Vehicles

  IEEE Transactions on Control Systems Technology · 2025-09-16

  articleOpen access

  This article presents a closed-form variable-structure control strategy with dual avoidance regions design for collision-free waypoint navigation and trajectory tracking of car-like vehicles. New Bézier curve-based collision risk assessment functions are introduced to improve curvature adjustment for both cooperative and noncooperative avoidance pairs of vehicles. The variable-structure control guides the vehicle’s state toward a switching surface, stabilizing the risk function’s rate of change and reducing conservative avoidance behavior. Safety-corrected tracking control enhances performance in dense obstacle environments. Based on the Lyapunov approach, technical guarantees are formulated and proved for the system’s convergence and collision-free maneuvering. Two simulations illustrate the approach: one for point-to-point tracking compared with other approaches, and the other for cooperative vehicles navigating a dense obstacle field. Finally, an experiment validates the control law’s applicability and effectiveness.

  PublisherDOI

• Collision-free formation control of UAVs via closed-form event-triggered strategy and generalized p-norm distance

  Aerospace Science and Technology · 2025-09-01 · 2 citations

  articleCorresponding
  PublisherDOI

• Switching-Based Cooperative Avoidance Control for Multi-Agent Quadrotor Dynamic Systems in Dense Environments

  Applied Sciences · 2025-12-18

  articleOpen accessSenior author

  This paper presents a control framework for multi-unmanned aerial vehicle systems that achieves safe and cooperative navigation in complex environments through a unified collision avoidance and trajectory guidance strategy. The principal innovation lies in the incorporation of velocity information into the design of a switching function, enabling more accurate assessment of collision risk and effectively reducing system conservativeness. Building upon this, an adaptive trajectory guidance mechanism is developed using collision avoidance information to ensure safe motion coordination among the vehicles. In addition, a closed-form solution for the dynamic system is derived, and its safety and stability are rigorously established through Lyapunov-based analysis. The effectiveness of the proposed framework is validated through simulation studies conducted on the MATLAB/Simulink platform (version R2020b), confirming reliable cooperative navigation in densely cluttered environments and guaranteeing dynamic safety.

  PublisherDOI

• Stability of Difference Equations and Long Short-Term Memory Neural Networks

  Synthesis lectures on mathematics and statistics · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• A closed-form avoidance control for safe maneuvering of multiple car-like vehicles

  Automatica · 2025-12-18

  articleSenior author
  PublisherDOI

• Control Strategies for Players with Discrete and Uncertain Observations

  Dynamic Games and Applications · 2025-07-31 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract In this paper, robust multiplayer games are generalized from a deterministic scenario with known multiplayer dynamics to a stochastic scenario where each player has uncertain dynamics and estimates other players’ dynamics through uncertain observations. A design approach to compute control strategy for each player given those observations, is provided. Information from other players is assumed to be available to each player only at discrete time instances and is assumed to be corrupted by noise. Having available only this limited and somewhat corrupted information due to its dependence on noise, each player has to make their own decisions based on estimated states of other players which results in scenarios that may range from cooperative to noncooperative. Decisions are integrated into designs of most appropriate actions, that is, control strategies of each player. The design is illustrated on a multiplayer pursuit-evasion simulation example.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Safe Coordination of Multiple Autonomous Vehicles

  NSF · $175k · 2008–2012

Frequent coauthors

• Abebe Geletu

  African Institute for Mathematical Sciences

  49 shared

• Bo Yang

  Suizhou Central Hospital

  49 shared

• Markus Vogl

  49 shared

• Kibru Teka

  Technische Universität Ilmenau

  49 shared

• Pu Li

  Four Seasons

  49 shared

• Ph Yang

  University of Liverpool

  49 shared

• Xing He

  Sichuan University

  49 shared

• Minyu Chen

  Centre National de la Recherche Scientifique

  49 shared

Education

• Ph.D., Electrical Engineering

  Santa Clara University

  2000

• MS, Electrical Engineering

  Santa Clara University

  1996

• BSEE, Control Systems, School of Electrical Engineering

  University of Belgrade

  1994