相关论文: Development and Assessment of a Nearly Autonomous …

The paper discusses an adaptive strategy to effectively control nonlinear manipulation motions of a dual arm robot (DAR) under system uncertainties including parameter variations, actuator nonlinearities and external disturbances. It is…

The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy…

As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provide. Because control systems directly affect the motion of…

In the past decade, society has experienced notable growth in a variety of technological areas. However, the Fourth Industrial Revolution has not been embraced yet. Industry 4.0 imposes several challenges which include the necessity of new…

A data acquisition (DAQ) system has been developed which will read out and control calorimeters serving as prototype systems for a future detector at an electron-positron linear collider. This is a modular, flexible and scalable DAQ system…

We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints. By performing the same task…

A fuel cell system must output a steady voltage as a power source in practical use. A neural network (NN) based model predictive control (MPC) approach is developed in this work to regulate the fuel cell output voltage with safety…

High energy physics experiments in KEK/Japan rush into over KHz trigger stage. Thus, we need a successor of the data acquisition(DAQ) system that replaces the CAMAC or FASTBUS systems. To meet these needs, we have developed a DAQ system…

Accurately modeling robot dynamics is crucial to safe and efficient motion control. In this paper, we develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing with a Model…

A network of nanomachines (NMs) can be used to build a target detection system for a variety of promising applications. They have the potential to detect toxic chemicals, infectious bacteria, and biomarkers of dangerous diseases such as…

As a benchmark exercise for testing software and methods developed in Hycon2 for decentralized and distributed control, we address the problem of designing the Automatic Generation Control (AGC) layer in power network systems. In…

This paper addresses the challenge of representing complex human action (HA) in a nuclear power plant (NPP) digital twin (DT) and minimizing latency in partial computation offloading (PCO) in sixth-generation-enabled computing in the…

This paper presents the Task-Parameter Nexus (TPN), a learning-based approach for online determination of the (near-)optimal control parameters of model-based controllers (MBCs) for tracking tasks. In TPN, a deep neural network is…

Using artificial neural-network machine learning (ANN-ML) to generate interatomic potentials has been demonstrated to be a promising approach to address the long-standing challenge of accuracy versus efficiency in molecular dynamics (MD)…

We propose a theoretical model that integrates a three-level $\Lambda$-type quantum heat engine with a vibrating nanomirror, where the connection is established via a laser field. In the presence of both hot and cold thermal photonic baths,…

Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods.…

In this paper, a method is proposed for on-line monitoring of the control updating period in fast-gradient-based Model Predictive Control (MPC) schemes. Such schemes are currently under intense investigation as a way to accommodate for…

The ngdp framework advanced topics are described. Namely we consider work with CAMAC hardware, "selfflow" nodes for the data acquisition systems with the As-Soon-As-Possible policy, ng_mm(4) as alternative to ng_socket(4), the control…

In this paper, near optimal tracking of a class of nonlinear systems is addressed. Adaptive (approximate) dynamic programming approach is used to calculate the optimal control in closed form. ADP (Adaptive (approximate) dynamic programming)…

Recently, non-autoregressive (NAT) models predict outputs in parallel, achieving substantial improvements in generation speed compared to autoregressive (AT) models. While performing worse on raw data, most NAT models are trained as student…