1 code implementation • 26 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.
no code implementations • 28 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.
no code implementations • 18 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.
1 code implementation • 15 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.
1 code implementation • 30 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.
1 code implementation • 16 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.
1 code implementation • 12 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.
no code implementations • 19 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.
no code implementations • 12 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.
no code implementations • 29 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.
no code implementations • 11 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).
no code implementations • 9 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.
no code implementations • 14 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.
no code implementations • 5 May 2022 • Zhe Du, Laura Balzano, Necmiye Ozay
Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time.
no code implementations • 11 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.
no code implementations • 13 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.
no code implementations • 25 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.
no code implementations • 24 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.
no code implementations • 4 Jul 2021 • Liren Yang, Necmiye Ozay
Zonotopes are widely used for over-approximating forward reachable sets of uncertain linear systems for verification purposes.
no code implementations • 26 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.
no code implementations • 18 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.
no code implementations • 19 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.
no code implementations • 19 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.
no code implementations • 9 Nov 2020 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a method for learning to satisfy uncertain constraints from demonstrations.
no code implementations • 18 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.
no code implementations • 3 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.
no code implementations • 25 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.
1 code implementation • 2 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
no code implementations • 8 Oct 2019 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations.
no code implementations • 17 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.
1 code implementation • 14 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.
no code implementations • 24 Jan 2017 • Sze Zheng Yong, Lingyun Gao, Necmiye Ozay
In this paper, we consider adaptive decision-making problems for stochastic state estimation with partial observations.
1 code implementation • 19 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