arXiv research

New approach generates optimal disturbances for controller verification.

SCPO learns robust policies without modeling disturbance, improving real-world task performance.

The paper analyzes how disturbances affect the convergence of algorithms in complex systems.

Study examines how disturbances affect financial returns in Austrian forests.

EGFC learns from streaming data to classify power quality disturbances.

Paper revisits set membership estimation for linear systems with relaxed disturbance bounds.

Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specific…

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time …

Safe exploration method for RL under disturbance ensures safety with probabilistic guarantees.

We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online control procedure that can do nearly as well as that of a procedure that has full knowledge of the disturbances in hindsight. Our main result…

Protocol learns pure quantum states with minimal disturbance.

New method identifies network dynamics and noise structure.

New method controls linear systems with adversarial disturbances.

New method controls linear systems with partial info and disturbances.

Power quality (PQ) analysis describes the non-pure electric signals that are usually present in electric power systems. The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features. Similar to other …

We introduce a new algorithm for online linear-quadratic control in a known system subject to adversarial disturbances. Existing regret bounds for this setting scale as $\sqrt{T}$ unless strong stochastic assumptions are imposed on the disturbance process. We give the first algorithm with logarithmic regret for arbitra…

WRAAC uses Wasserstein distance for robust reinforcement learning.

Bayesian method optimizes rescheduling for multipurpose batch processes with incomplete look-ahead information.

Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QP in real-time can be a computationally expensive process …

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system…

Develops robust MDPs for unknown disturbances with performance guarantees.

Paper proposes tensor-based method for semiconductor manufacturing process control.

In this work we seek for an approach to integrate safety in the learning process that relies on a partly known state-space model of the system and regards the unknown dynamics as an additive bounded disturbance. We introduce a framework for safely learning a control strategy for a given system with an additive disturba…

A method to minimize regret in multi-agent control systems with adversarial disturbances.

Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of potential disturbance. In this work, we use realistic crossbar simulations to highlight that compact implementations of deep neural networks a…

New method recovers causal order from dependent data.

Study online control of unknown time-varying systems with negative and positive results.

Paper reduces sample complexity for bilinear systems identification to nearly constant.

In this paper, we utilize information theory to study the fundamental performance limitations of generic feedback systems, where both the controller and the plant may be any causal functions/mappings while the disturbance can be with any distributions. More specifically, we obtain fundamental $\mathcal{L}_p$ bounds on …

MFRL-BI controls manufacturing processes without needing accurate models.

New method uses small perturbations to improve representation learning from few labels.

Paper develops online learning-based risk-averse MPC for uncertain systems.

Gradient boosting for spatial regression models improves prediction accuracy.

We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series eco…

Improves reinforcement learning policies for robustness.

This work uses QPGPs to improve ILC performance in repetitive tasks.

Paper develops a neural-fuzzy controller for GPS-intelligent buoys.

New method relaxes optimization problems to find solutions more reliably.

Graph neural networks detect structural perturbations from time series data.

Optimistic Q-learning reduces sample complexity for systems with known dynamics.

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement Learning (RL) algorithm for a wide range of tasks, such as Atari games and robot control. The agent learns policies and value function through trial-and-error interactions with the environment until converging to an optimal policy. Robustness and s…

We study the regular conditional law of mixed Gaussian Volterra processes under the influence of model disturbances. More precisely, we study prediction of Gaussian Volterra processes driven by a Brownian motion in a case where the Brownian motion is not observable, but only a noisy version is observed. As an applicati…

In processing and manufacturing industries, there has been a large push to produce higher quality products and ensure maximum efficiency of processes. This requires approaches to effectively detect and resolve disturbances to ensure optimal operations. While the control system can compensate for many types of disturban…

SOOTT framework optimizes target tracking with robust and learning-augmented algorithms.

Adversarial training improves linear regression solutions, revealing sparsity and abrupt interpolation.

Improves magnetic field mapping using an array of magnetometers with noisy input.

We propose a sliding surface for systems on the Lie group $SO(3)\times \mathbb{R}^3$ . The sliding surface is shown to be a Lie subgroup. The reduced-order dynamics along the sliding subgroup have an almost globally asymptotically stable equilibrium. The sliding surface is used to design a sliding-mode controller for t…

In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with c…