Search Results for author: Necmiye Ozay

Found 33 papers, 8 papers with code

A Simulation Preorder for Koopman-like Lifted Control Systems

1 code implementation26 Jan 2024 Antoine Aspeel, Necmiye Ozay

This paper introduces a simulation preorder among lifted systems, a generalization of finite-dimensional Koopman approximations (also known as approximate immersions) to systems with inputs.

Relation

Properties of Immersions for Systems with Multiple Limit Sets with Implications to Learning Koopman Embeddings

no code implementations28 Dec 2023 Zexiang Liu, Necmiye Ozay, Eduardo D. Sontag

Linear immersions (or Koopman eigenmappings) of a nonlinear system have wide applications in prediction and control.

On the Hardness of Learning to Stabilize Linear Systems

no code implementations18 Nov 2023 Xiong Zeng, Zexiang Liu, Zhe Du, Necmiye Ozay, Mario Sznaier

Inspired by the work of Tsiamis et al. \cite{tsiamis2022learning}, in this paper we study the statistical hardness of learning to stabilize linear time-invariant systems.

A Low Rank Approach to Minimize Sensor-to-Actuator Communication in Finite Horizon Output Feedback

1 code implementation15 Nov 2023 Antoine Aspeel, Jakob Nylof, Jing Shuang Li, Necmiye Ozay

In this work, we are interested in designing a controller that can be implemented with a minimum number of sensor-to-actuator messages, while satisfying safety constraints over a finite horizon.

A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles

1 code implementation30 Oct 2023 Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay

By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate the problem of safety-guaranteed preference learning based on pairwise comparisons and propose an approach to solve this learning problem.

Autonomous Vehicles

Can Transformers Learn Optimal Filtering for Unknown Systems?

1 code implementation16 Aug 2023 Haldun Balim, Zhe Du, Samet Oymak, Necmiye Ozay

Particularly, we train the transformer using various distinct systems and then evaluate the performance on unseen systems with unknown dynamics.

Koopman-inspired Implicit Backward Reachable Sets for Unknown Nonlinear Systems

1 code implementation12 Jun 2023 Haldun Balim, Antoine Aspeel, Zexiang Liu, Necmiye Ozay

Koopman liftings have been successfully used to learn high dimensional linear approximations for autonomous systems for prediction purposes, or for control systems for leveraging linear control techniques to control nonlinear dynamics.

Quantifying the Value of Preview Information for Safety Control

no code implementations19 Mar 2023 Zexiang Liu, Necmiye Ozay

Safety-critical systems, such as autonomous vehicles, often incorporate perception modules that can anticipate upcoming disturbances to system dynamics, expecting that such preview information can improve the performance and safety of the system in complex and uncertain environments.

Autonomous Vehicles

Probabilistic Constraint Construction for Network-safe Load Coordination

no code implementations12 Jan 2023 Sunho Jang, Necmiye Ozay, Johanna L Mathieu

Distributed Energy Resources (DERs) can provide balancing services to the grid, but their power variations might cause voltage and current constraint violations in the distribution network, compromising network safety.

Finite Sample Identification of Bilinear Dynamical Systems

no code implementations29 Aug 2022 Yahya Sattar, Samet Oymak, Necmiye Ozay

This motivates the problem of learning bilinear systems from a single trajectory of the system's states and inputs.

On the Convergence of the Backward Reachable Sets of Robust Controlled Invariant Sets For Discrete-time Linear Systems

no code implementations11 Jul 2022 Zexiang Liu, Necmiye Ozay

This paper considers discrete-time linear systems with bounded additive disturbances, and studies the convergence properties of the backward reachable sets of robust controlled invariant sets (RCIS).

Efficient Backward Reachability Using the Minkowski Difference of Constrained Zonotopes

no code implementations9 Jul 2022 Liren Yang, Hang Zhang, Jean-Baptiste Jeannin, Necmiye Ozay

This Minkowski difference needs to be represented as a constrained zonotope to enable subsequent computation, but, as we show, it is impossible to find a polynomial-sized representation for it in polynomial time.

Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory

no code implementations14 Jun 2022 Glen Chou, Necmiye Ozay, Dmitry Berenson

We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (e. g., RGB-D images) and a learned perception module in the feedback control loop.

Motion Planning valid

Mode Reduction for Markov Jump Systems

no code implementations5 May 2022 Zhe Du, Laura Balzano, Necmiye Ozay

Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time.

An Invariant Set Construction Method, Applied to Safe Coordination of Thermostatic Loads

no code implementations11 Feb 2022 Sunho Jang, Necmiye Ozay, Johanna L. Mathieu

In this paper, we introduce a strategy to obtain an implicit representation of a controlled invariant set for a collection of switched subsystems, and construct a safety-guaranteed controller to coordinate the subsystems using the representation.

Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds

no code implementations13 Nov 2021 Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak

Combining our sample complexity results with recent perturbation results for certainty equivalent control, we prove that when the episode lengths are appropriately chosen, the proposed adaptive control scheme achieves $\mathcal{O}(\sqrt{T})$ regret, which can be improved to $\mathcal{O}(polylog(T))$ with partial knowledge of the system.

Automaton-based Implicit Controlled Invariant Set Computation for Discrete-Time Linear Systems

no code implementations25 Sep 2021 Zexiang Liu, Tzanis Anevlavis, Necmiye Ozay, Paulo Tabuada

In this paper, we derive closed-form expressions for implicit controlled invariant sets for discrete-time controllable linear systems with measurable disturbances.

Synthesis-guided Adversarial Scenario Generation for Gray-box Feedback Control Systems with Sensing Imperfections

no code implementations24 Jul 2021 Liren Yang, Necmiye Ozay

We develop an algorithm that searches for "adversarial scenarios", which can be thought of as the strategy for the adversary representing the noise and disturbances, that lead to safety violations.

Scalable Zonotopic Under-approximation of Backward Reachable Sets for Uncertain Linear Systems

no code implementations4 Jul 2021 Liren Yang, Necmiye Ozay

Zonotopes are widely used for over-approximating forward reachable sets of uncertain linear systems for verification purposes.

Certainty Equivalent Quadratic Control for Markov Jump Systems

no code implementations26 May 2021 Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Samet Oymak, Necmiye Ozay

Real-world control applications often involve complex dynamics subject to abrupt changes or variations.

Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems

no code implementations18 Apr 2021 Glen Chou, Necmiye Ozay, Dmitry Berenson

We derive a trajectory tracking error bound for a contraction-based controller that is subjected to this model error, and then learn a controller that optimizes this tracking bound.

Deformable Object Manipulation Motion Planning +1

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

no code implementations19 Mar 2021 Danny Weyns, Bradley Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation.

BIG-bench Machine Learning

On the Value of Preview Information For Safety Control

no code implementations19 Mar 2021 Zexiang Liu, Necmiye Ozay

However, little work has been done for analyzing the value of preview information for safety control for systems with continuous state spaces.

Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants

no code implementations18 Oct 2020 Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson

Our method imposes the feedback law existence as a constraint in a sampling-based planner, which returns a feedback policy around a nominal plan ensuring that, if the Lipschitz constant estimate is valid, the true system is safe during plan execution, reaches the goal, and is ultimately invariant in a small set about the goal.

Motion Planning valid

Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations

no code implementations3 Jun 2020 Glen Chou, Necmiye Ozay, Dmitry Berenson

We present a method for learning multi-stage tasks from demonstrations by learning the logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula.

Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty

no code implementations25 Jan 2020 Glen Chou, Necmiye Ozay, Dmitry Berenson

We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner.

From Drinking Philosophers to Asynchronous Path-Following Robots

1 code implementation2 Jan 2020 Yunus Emre Sahin, Necmiye Ozay

We first show that this problem can be reformulated as a distributed resource allocation problem and, in particular, as an instance of the well-known Drinking Philosophers Problem (DrPP).

Robotics Multiagent Systems Systems and Control Systems and Control

Learning Parametric Constraints in High Dimensions from Demonstrations

no code implementations8 Oct 2019 Glen Chou, Necmiye Ozay, Dmitry Berenson

We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations.

Vocal Bursts Intensity Prediction

Learning Constraints from Demonstrations

no code implementations17 Dec 2018 Glen Chou, Dmitry Berenson, Necmiye Ozay

We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations.

Non-asymptotic Identification of LTI Systems from a Single Trajectory

1 code implementation14 Jun 2018 Samet Oymak, Necmiye Ozay

We consider the problem of learning a realization for a linear time-invariant (LTI) dynamical system from input/output data.

Guaranteed Model-Based Fault Detection in Cyber-Physical Systems: A Model Invalidation Approach

1 code implementation19 Sep 2016 Farshad Harirchi, Necmiye Ozay

This paper presents a sound and complete fault detection approach for cyber-physical systems represented by hidden-mode switched affine models with time varying parametric uncertainty.

Optimization and Control

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