Results 41 to 50 of about 303,155 (330)
, 2018 Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties.
Duivenvoorden, Rikky R. P. R. +3 more
core +1 more source
Learning with Delayed Synaptic Plasticity [PDF]
, 2019 The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning ...
Fletcher, George +4 more
core +2 more sources
Cluster-based feedback control of turbulent post-stall separated flows
, 2018 We propose a novel model-free self-learning cluster-based control strategy for general nonlinear feedback flow control technique, benchmarked for high-fidelity simulations of post-stall separated flows over an airfoil. The present approach partitions the
Brunton, Steven L. +5 more
core +1 more source
IEEE transactions on industrial electronics (1982. Print), 2020 Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power-to-weight ratio features. However, the nonlinearity and time-varying nature of PAMs make it challenging to maintain
Qingsong Ai +6 more
semanticscholar +1 more source
Rethinking plastic waste: innovations in enzymatic breakdown of oil‐based polyesters and bioplastics
FEBS Open Bio, EarlyView.Plastic pollution remains a critical environmental challenge, and current mechanical and chemical recycling methods are insufficient to achieve a fully circular economy. This review highlights recent breakthroughs in the enzymatic depolymerization of both oil‐derived polyesters and bioplastics, including high‐throughput protein engineering, de novo ...
Elena Rosini +2 more
wiley +1 more source
IEEE Access, 2023 This paper studies the event-triggered saturated adaptive iterative learning control (ETSAILC) problem for the fractional-order multi-agent systems (FOMASs) subject to the local Lipschitz nonlinearities and the input saturation.
Yusen Liu, Liming Wang
doaj +1 more source
A Survey on Compiler Autotuning using Machine Learning
, 2018 Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems.
Ashouri, Amir H. +4 more
core +1 more source
Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system
FEBS Open Bio, EarlyView.An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková +3 more
wiley +1 more source
IEEE Access, 2022 In this paper, a high-order internal model based adaptive iterative learning control scheme is proposed to solve the trajectory tracking problem for a class of nonlinear systems with time-iteration-varying parametric uncertainties which are generated ...
Zhi Yang +4 more
doaj +1 more source
Adaptive Iterative Learning Control for High Precision Motion Systems
IEEE Transactions on Control Systems Technology, 2008 Iterative learning control (ILC) is a very effective technique to reduce systematic errors that occur in systems that repetitively perform the same motion or operation. However, several characteristics have prevented standard ILC from being widely used for high precision motion systems.
Rotariu, I. (author) +2 more
openaire +2 more sources