Results 31 to 40 of about 2,868 (137)

Physics‐Informed Neural Networks for Battery Degradation Prediction Under Random Walk Operations

Quality and Reliability Engineering International, EarlyView.
ABSTRACT This study addresses the challenge of predicting the state of health (SoH) and capacity degradation in Battery Energy Storage Systems (BESS) under highly variable conditions induced by frequent control adjustments. In environments where random walk behavior prevails due to stochastic control commands, conventional estimation methods often ...
Alaa Selim, Huadong Mo, Hemanshu Pota, Daoyi Dong   +3 more
wiley   +1 more source

PID‐Like Robust Control of Non‐Minimum Phase Robotic Manipulators

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT This paper proposes an output‐feedback tracking controller for non‐minimum phase nonlinear systems with unknown uncertainties and external disturbances, where not all states are measurable, and the zero dynamics are unstable. The approach combines a backstepping‐based stabilizing state‐feedback law with a cascade extended high‐gain observer ...
Mohammad Al Saaideh, Almuatazbellah Boker, Mohammad Al Janaideh   +2 more
wiley   +1 more source

Neural Network‐Based Adaptive Super‐Twisting Control for Perturbed Nonlinear Systems: Lyapunov Design

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT A newly developed robust control strategy based on an adaptive neural network (ANN) estimator performed in concert with adaptive super‐twisting (ASTW) control is presented. The proposed adaptive robust control algorithm is applied to a class of perturbed nonlinear systems and features a significantly decreased control gain with respect to the ...
Mohammad Javad Mirzaei, Mohamed Assaad Hamida, Yuri Shtessel, Franck Plestan   +3 more
wiley   +1 more source

Recursive Feasibility of Nonlinear Stochastic Model Predictive Control With Gaussian Process Dynamics

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf, Andreas Völz, Knut Graichen   +2 more
wiley   +1 more source

Synthesizing Interacting Model‐Based Optimal Control and Model‐Free Learning Approaches for Nonlinear Systems

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT In this paper, we consider the optimal control problem for an unknown continuous‐time nonlinear system, and present a framework that integrates model‐based and model‐free methods to solve it. Each approach offers distinct advantages: model‐based techniques provide offline synthesis and data efficiency, while model‐free procedures excel at ...
Surabhi Athalye, Kyriakos G. Vamvoudakis, Panos J. Antsaklis   +2 more
wiley   +1 more source

Initial State Privacy of Nonlinear Systems on Riemannian Manifolds

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley   +1 more source

A Formation Inversion Algorithm Based on Collaborative Fuzzy Gradient Neural Dynamics for Natural Gamma Logging While Drilling

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT A formation inversion algorithm with real‐time performance and accuracy is crucial for natural gamma logging while drilling (LWD). However, traditional inversion algorithms are often limited by high computational resource consumption and insufficient accuracy.
Juntao Liu, Ruoxiao Liu, Wenxin Li, Zhenming Su, Long Jin   +4 more
wiley   +1 more source

On Metric Choice in Dimension Reduction for Fréchet Regression

International Statistical Review, EarlyView.
Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data.
Abdul‐Nasah Soale, Congli Ma, Siyu Chen, Obed Koomson   +3 more
wiley   +1 more source

A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces

International Statistical Review, EarlyView.
Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri, Bernardo Nipoti, Emilio Porcu, Philip White   +3 more
wiley   +1 more source

Gradual Changes in Functional Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We consider the problem of detecting gradual changes in the sequence of mean functions from a not necessarily stationary functional time series. Our approach is based on the maximum deviation (calculated over a given time interval) between a benchmark function and the mean functions at different time points.
Patrick Bastian, Holger Dette
wiley   +1 more source