Results 61 to 70 of about 12,702 (221)

Data‐Driven and Physics‐Informed Modeling Perspectives for Soft Manipulators: From SINDy to Hybrid Intelligence

Advanced Science, EarlyView.
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui, Yunyi Wang, Teng Zhang, Yang Chen, Min Pan   +4 more
wiley   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

A Hybrid Magnetic Actuation System for Single‐Input Multistates Stabilization: Application to pH‐Responsive Drug Delivery

Advanced Intelligent Systems, EarlyView.
This study presents an integrated proof‐of‐concept robotic platform for targeted delivery applications. A hybrid magnetic actuation system achieves single‐input (z) multistate stabilization (x, y, ϕ, and θ) for precise capsule transport with submillimeter positioning accuracy.
Sejun Park, Hanbi Park, Suho Lee, Bohyeon Kim, Jiseong Ryu, ChangKyu Yoon, Donghoon Son   +6 more
wiley   +1 more source

Performance improvement of discrete‐time linear‐quadratic regulators applied to uncertain linear systems using the Tikhonov regularization method

Asian Journal of Control, EarlyView.
Abstract The linear‐quadratic regulator (LQR) problem of optimal control of an uncertain discrete‐time linear system (DTLS) is revisited in this paper from the perspective of Tikhonov regularization. We show that an optimally chosen regularization parameter reduces, compared to the classical LQR, the values of a scalar error function, as well as the ...
Fernando Pazos, Amit Bhaya
wiley   +1 more source

Policy Evaluation in Distributional LQR

IEEE Transactions on Automatic Control
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL. At the same time, a main challenge in DRL is that policy evaluation in DRL typically relies on the representation of the
Zifan Wang 0002, Yulong Gao 0001, Siyi Wang, Michael M. Zavlanos, Alessandro Abate, Karl Henrik Johansson   +5 more
openaire   +4 more sources

On the design of LQR kernels for efficient controller learning [PDF]

2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective ...
Alonso Marco, Philipp Hennig, Stefan Schaal, Sebastian Trimpe   +3 more
openaire   +5 more sources

Novel Results on Output-Feedback LQR Design [PDF]

, 2023
This paper provides novel developments in output-feedback stabilization for linear time-invariant systems within the linear quadratic regulator (LQR) framework.
Ilka, Adrian, Murgovski, Nikolce, Murgovski, Nikolce,, Ilka, Adrian,, Adrian Ilka, Nikolce Murgovski   +5 more
core   +1 more source

Research on Tractor Trajectory Tracking Control Based on a Dual-Motor Steer-by-Wire System

智慧农业
[Objective]Conventional steering systems for autonomous tractors often suffer from insufficient precision and robustness, making it difficult to meet the demands of high-accuracy trajectory tracking.
LI Lei, PAN Liang, DONG Jiwei, CAO Zhonghua, LUO Xingfa, ZHAN Xiaomei, LI Yali, SUN Zhiqiang   +7 more
doaj   +1 more source

Multi-objective optimization of LQR control quarter car suspension system using genetic algorithm [PDF]

FME Transactions, 2016
In this paper, genetic algorithm (GA) based multi-objective optimization technique is presented to search optimum weighting matrix parameters of linear quadratic regulator (LQR). Macpherson strut suspension system is implemented for study.
Nagarkar M.P., Patil Vikhe G.J.
doaj  

Deadbeat Robust Model Predictive Control for Linear Parameter‐Varying Systems

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT The concept of Deadbeat Robust Model Predictive Control (DRMPC) has recently been established for linear systems, providing full system theoretic guarantees, while reducing the computational and conceptual complexity. This article extends DRMPC to systems with multiplicative uncertainty (“robust linear”) and systems that depend on a (slowly ...
Georg Schildbach, Hossam S. Abbas
wiley   +1 more source