About

Melkior Ornik is an Assistant Professor in Aerospace Engineering at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Electrical and Computer Engineering from the University of Toronto, earned in 2017, and has academic positions including Research Associate and Postdoctoral Fellow at the University of Texas at Austin. His research interests encompass autonomous systems, control of systems operating in complex, uncertain, or changing environments, safety-critical control, and learning methods in control. His work focuses on the control and analysis of space systems, unmanned aerial vehicles, and the development of resilient and safe control strategies for complex dynamical systems.

Research topics

• Artificial Intelligence
• Computer Science
• Psychology
• Social psychology

Selected publications

• Energetic Resilience under Temporal Logic Specifications

  ArXiv.org · 2026-04-03

  articleOpen access

  In environments with uncertainties or undesirable influences, control systems can require additional energy to achieve their task while remaining resilient to these influences. In this paper, we present an energetic resilience metric that quantifies the maximal additional energy used by a system under undesired effects, while satisfying complex specifications encoded through temporal logic. We prove that this metric satisfies properties that enable its computation even for compositions of these specifications, thus allowing considerations of sequential reachability and safety tasks. For specifications related to finite-horizon reachability and safety, we describe how synthesizing a control input and computing this metric reduces to solving efficient quadratic programs. Two case studies on a fighter-jet model and a planar mobile robot illustrate how the synthesized control inputs satisfy given specifications despite undesired and potentially adversarial effects. Further, we demonstrate how the energetic resilience metric varies with the initial state as well as the magnitude of undesired effects.

  PublisherOA PDF

• Energetic Resilience under Temporal Logic Specifications

  arXiv (Cornell University) · 2026-04-03

  preprintOpen access

  In environments with uncertainties or undesirable influences, control systems can require additional energy to achieve their task while remaining resilient to these influences. In this paper, we present an energetic resilience metric that quantifies the maximal additional energy used by a system under undesired effects, while satisfying complex specifications encoded through temporal logic. We prove that this metric satisfies properties that enable its computation even for compositions of these specifications, thus allowing considerations of sequential reachability and safety tasks. For specifications related to finite-horizon reachability and safety, we describe how synthesizing a control input and computing this metric reduces to solving efficient quadratic programs. Two case studies on a fighter-jet model and a planar mobile robot illustrate how the synthesized control inputs satisfy given specifications despite undesired and potentially adversarial effects. Further, we demonstrate how the energetic resilience metric varies with the initial state as well as the magnitude of undesired effects.

  PublisherDOI

• Optimizing agricultural order fulfillment systems: A hybrid tree search approach

  Engineering Applications of Artificial Intelligence · 2025-10-15 · 1 citations

  articleSenior author
  PublisherDOI

• Numerical Estimation of the Sensitivity of Optimal Plant Design to Control Perturbations

  2025-08-17

  article

  Abstract Control co-design (CCD) methods treat plant and control design in an integrated manner. Control co-design problems typically present design coupling, that is, control and plant design decisions affect each other. Quantitative design coupling information can be used to assist in problem formulation, solution strategy selection and can provide new design insights. This work focuses on the estimation of control-plant coupling, that is, the effect of control decisions on optimal plant design. The type of perturbation that affects the control inputs may be known a priori only in some specific cases. In this work, we estimate control-plant coupling for the general case where the nature of the perturbation is unknown. The procedure is based on the use of the Fourier series to approximate the perturbation function. Two methods are investigated to determine the Fourier coefficients, with the goal of maximally efficiently determining sensitivity to a wide variety of perturbations. Two examples demonstrate the use of this procedure, and show that a good estimate of the coupling strength may be obtained with a small sampling of perturbations.

  PublisherDOI

• Virtual Force-Based Routing of Modular Agents on a Graph

  arXiv (Cornell University) · 2025-05-02

  preprintOpen accessSenior author

  Modular vehicles present a novel area of academic and industrial interest in the field of multi-agent systems. Modularity allows vehicles to connect and disconnect with each other mid-transit which provides a balance between efficiency and flexibility when solving complex and large scale tasks in urban or aerial transportation. This paper details a generalized scheme to route multiple modular agents on a graph to a predetermined set of target nodes. The objective is to visit all target nodes while incurring minimum resource expenditure. Agents that are joined together will incur the equivalent cost of a single agent, which is motivated by the logistical benefits of traffic reduction and increased fuel efficiency. To solve this problem, we introduce a novel algorithm that seeks to balance the optimality of the path that every single module takes and the cost benefit of joining modules. Our approach models the agents and targets as point charges, where the modules take the path of highest attractive force from its target node and neighboring agents. We validate our approach by simulating multiple modular agents along real-world transportation routes in the road network of Champaign-Urbana, Illinois, USA. The proposed method easily exceeds the available benchmarks and illustrates the benefits of modularity in multi-target planning problems.

  PublisherOA PDFDOI

• Sum-of-Squares Data-driven Robustly Stabilizing and Contracting Controller Synthesis for Polynomial Nonlinear Systems

  2025-07-08 · 1 citations

  articleSenior author

  This work presents a computationally efficient approach to data-driven robust contracting controller synthesis for polynomial control-affine systems based on a sum-of-squares program. In particular, we consider the case in which a system alternates between periods of high-quality sensor data and low-quality sensor data. In the high-quality sensor data regime, we focus on robust system identification based on the data informativity framework. In low-quality sensor data regimes we employ a robustly contracting controller that is synthesized online by solving a sum-of-squares program based on data acquired in the high-quality regime, so as to limit state deviation until high-quality data is available. This approach is motivated by real-life control applications in which systems experience periodic data blackouts or occlusion, such as autonomous vehicles undergoing loss of GPS signal or solar glare in machine vision systems. We apply our approach to a planar unmanned aerial vehicle model subject to an unknown wind field, demonstrating its uses for verifiably tight control on trajectory deviation.

  PublisherDOI

• Analysis of the Unscented Transform Controller for Systems with Bounded Nonlinearities

  ArXiv.org · 2025-04-11

  preprintOpen accessSenior author

  In this paper, we present an analysis of the Unscented Transform Controller (UTC), a technique to control nonlinear systems motivated as a dual to the Unscented Kalman Filter (UKF). We consider linear, discrete-time systems augmented by a bounded nonlinear function of the state. For such systems, we review 1-step and N-step versions of the UTC. Using a Lyapunov-based analysis, we prove that the states and inputs converge to a bounded ball around the origin, whose radius depends on the bound on the nonlinearity. Using examples of a fighter jet model and a quadcopter, we demonstrate that the UTC achieves satisfactory regulation and tracking performance on these nonlinear models.

  PublisherOA PDFDOI

• Mode-Prefix-Based Control of Switched Linear Systems With Applications to Fault Tolerance

  IEEE Control Systems Letters · 2025-01-01 · 1 citations

  articleOpen accessSenior author

  In this paper, we consider the problem of designing prefix-based optimal controllers for switched linear systems over finite horizons. This problem arises in fault-tolerant control, when system faults result in abrupt changes in dynamics. We consider a class of mode-prefix-based linear controllers that depend only on the history of the switching signal. The proposed optimal control problems seek to minimize both expected performance and worst-case performance over switching signals. We show that this problem can be reduced to a convex optimization problem. To this end, we synthesize one controller for each switching signal under a prefix constraint that ensures consistency between controllers. Then, system level synthesis is used to obtain a convex program in terms of the system-level parameters. In particular, it is shown that the prefix constraints are linear in terms of the system-level parameters. Finally, we apply this framework for optimal control of a fighter jet model suffering from system faults, illustrating how fault tolerance is ensured.

  PublisherOA PDFDOI

• Autonomous Excavation of Challenging Terrain using Oscillatory Primitives and Adaptive Impedance Control

  2025-05-19

  article

  This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a standard penetrate-drag-scoop motion pattern. Inspired by human excavation strategies, our approach incorporates oscillatory rotation elements - including swivel, twist, and dive motions - to break up compacted, tangled grains and reduce jamming. We also present an adaptive impedance control method, the Reactive Attractor Impedance Controller (RAIC), that adapts a motion trajectory to unexpected forces during loading in a manner that tracks a trajectory closely when loads are low, but avoids excessive loads when significant resistance is met. Our method is evaluated on four terrains using a robotic arm, demonstrating improved excavation performance across multiple metrics, including volume scooped, protective stop rate, and trajectory completion percentage.

  PublisherDOI

• Lexicographic Multi-Objective Stochastic Shortest Path with Mixed Max-Sum Costs

  ArXiv.org · 2025-12-14

  preprintOpen accessSenior author

  We study the Stochastic Shortest Path (SSP) problem for autonomous systems with mixed max-sum cost aggregations under Linear Temporal Logic constraints. Classical SSP formulations rely on sum-aggregated costs, which are suitable for cumulative quantities such as time or energy but fail to capture bottleneck-style objectives such as avoiding high-risk transitions, where performance is determined by the worst single event along a trajectory. Such objectives are particularly important in safety-critical systems, where even one hazardous transition can be unacceptable. To address this limitation, we introduce max-aggregated objectives that minimize the bottleneck cost, i.e., the maximum one-step cost along a trajectory. We show that standard Bellman equations on the original state space do not apply in this setting and propose an augmented MDP with a state variable tracking the running maximum cost, together with a value iteration algorithm. We further identify a cyclic policy phenomenon, where zero-marginal-cost cycles prevent goal reaching under max-aggregation, and resolve it via a finite-horizon formulation. To handle richer task requirements, linear temporal logic specifications are translated into deterministic finite automata and combined with the system to construct a product MDP. We propose a lexicographic value iteration algorithm that handles mixed max-sum objectives under lexicographic ordering on this product MDP. Gridworld case studies demonstrate the effectiveness of the proposed framework.

  PublisherOA PDFDOI

Frequent coauthors

• Ufuk Topcu

  The University of Texas at Austin

  42 shared

• Jean-Baptiste Bouvier

  23 shared

• Pranay Thangeda

  23 shared

• Ilan Shomorony

  University of Illinois Urbana-Champaign

  17 shared

• Mr Peterson

  University of Illinois Urbana-Champaign

  16 shared

• Alejandro D. Domínguez-García

  16 shared

• Daniel Liberzon

  University of Illinois Urbana-Champaign

  16 shared

• Georgios Fellouris

  University of Illinois Urbana-Champaign

  16 shared

Labs

• Combustion, Flow, and Plasma Interaction LaboratoryPI

Education

• Ph.D., Aerospace Engineering

  University of Illinois Urbana-Champaign

Awards & honors

• Alumni Awards and Endowments
• Alumni Loyalty Award
• Distinguished Alumnus Award
• Alumni Award for Distinguished Service