arXiv research

Paper proposes a new model for better engine control.

Anticipatory model generates music with control over events.

Optimizes dividend policies in a Brownian model with controlled rates.

Unified control theory and machine learning for safety in uncertain systems.

Safe RL-based vibration control using LQR guidance.

Survey combines FL and control for better adaptability and privacy.

Controller seeks informative system observations to predict nonlinear dynamics.

The OGY method is one of control methods for a chaotic system. In the method, we have to calculate a stabilizing periodic orbit embedded in its chaotic attractor. Thus, we cannot use this method in the case where a precise mathematical model of the chaotic system cannot be identified. In this case, the delayed feedback…

Researchers develop a method to control nonlinear systems with Koopman operator regression.

Framework simplifies vision-based control and goal discovery.

Hybrid controller combines model-based and policy-based reinforcement learning.

This paper compares model-based and model-free control methods using neural networks.

Formula adjusts steady-state models for control confounding.

We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constr…

Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed into the model. This paper introduces a plug-…

Deep reinforcement learning controls drones without model knowledge.

Develops a control framework for systemic risk under uncertainty.

This paper addresses the optimal control problem known as the Linear Quadratic Regulator in the case when the dynamics are unknown. We propose a multi-stage procedure, called Coarse-ID control, that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then d…

High-dimensional models can outperform simpler ones in causal inference.

Despite decades of research and recent progress in adaptive control and reinforcement learning, there remains a fundamental lack of understanding in designing controllers that provide robustness to inherent non-asymptotic uncertainties arising from models estimated with finite, noisy data. We propose a robust adaptive …

MFRL-BI controls manufacturing processes without needing accurate models.

Stabilizes complex systems using diffusion models trained on Lyapunov functions.

Develops a new model for controllable and realistic traffic simulation.

Greenhouse environment is the key to influence crops production. However, it is difficult for classical control methods to give precise environment setpoints, such as temperature, humidity, light intensity and carbon dioxide concentration for greenhouse because it is uncertain nonlinear system. Therefore, an intelligen…

ControlVAE improves VAE performance by adding a controller to tune hyperparameters.

Combines Gaussian processes and polynomial chaos for stochastic control.

In this paper, a MIMO simulated annealing SA based Q learning method is proposed to control a line follower robot. The conventional controller for these types of robots is the proportional P controller. Considering the unknown mechanical characteristics of the robot and uncertainties such as friction and slippery surfa…

SHADOWCAST generates graphs with user-specified attributes.

A RL-based method adds conditional controls to pre-trained diffusion models.

New framework for online control in evolving populations.

CNN identifies nonlinear human posture control models efficiently.

Paper proposes a new method to optimize robot body structure and control policy.

Study shows how to control jump-diffusion processes with stable feedback controls in reinforcement learning.

A model optimizes carbon emission reduction and allowance purchasing for companies.

Optimizes control of infectious disease spread using stochastic methods.

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

A new framework for generative modeling using controlled vector fields.

Optimal control in latent factor models uses Tsallis entropy for exploration.

Optimizes control of noisy discrete systems without system matrix knowledge.

The paper presents a model-free method for stabilizing unknown control systems.

Paper uses VAEs to control IVS features for financial modeling.

Physics-informed learning framework for pH systems and EB-PBC control.

In stochastic control problems delicate issues arise when the controlled system can jump due to both exogenous shocks and endogenous controls. Here one has to specify what the controller knows when about the exogenous shocks and how and when she can act on this information. We propose to use Meyer-$σ$-fields as a flexi…

RL applied to TCLs for power consumption control.

Framework calibrates ML models for risk control in various tasks.

Just as an explicit parameterisation of system dynamics by state, i.e., a choice of coordinates, can impede the identification of general structure, so it is too with an explicit parameterisation of system dynamics by control. However, such explicit and fixed parameterisation by control is commonplace in control theory…

Bayesian optimisation tackles stochastic MPC hyper-parameter tuning.

LqgOpt learns optimal control in unknown LQG systems with minimal regret.