This paper considers the compensation control problem for a class of nonlinear discrete-time systems subject to bounded disturbances. With the help of the dynamic linearization technique (DLT), an equivalent data model to the unknown disturbed controlled plant is first established. Based on the data model, two data-driven controllers are designed through novel disturbance-related compact-form and partial-form DLT, which are equivalent to the unknown ideal compensation controller in theory. Adaptive gains designed for the proposed controllers are time-varying and are adaptively updated by directly utilizing the I/O data without involving any model information of the controlled plant, making both controllers purely data-driven adaptive disturbance compensation controllers. Further, in practice, unmeasurable disturbances are commonly encountered due to expensive measuring instruments, unreliable performance or large lags. Therefore, both proposed control laws provide solutions for measurable disturbance (MD) and unmeasurable disturbance (UD) in a unified framework, where the time-varying adaptive gains fuse more system dynamics when disturbance is completely unknown except for some boundedness. The stability of the proposed controllers are strictly guaranteed, and their effectiveness and applicability are verified by a numerical simulation and a distillation column.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.isatra.2022.09.033 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets.
Front Artif Intell
January 2025
Lawrence Livermore National Laboratory, Livermore, CA, United States.
Packed columns are commonly used in post-combustion processes to capture CO emissions by providing enhanced contact area between a CO-laden gas and CO-absorbing solvent. To study and optimize solvent-based post-combustion carbon capture systems (CCSs), computational fluid dynamics (CFD) can be used to model the liquid-gas countercurrent flow hydrodynamics in these columns and derive key determinants of CO-capture efficiency. However, the large design space of these systems hinders the application of CFD for design optimization due to its high computational cost.
View Article and Find Full Text PDFA data-driven state identification method for intelligent control of the joint station export system.
Sci Rep
January 2025
School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu, 610500, Sichuan, China.
As a necessary part of intelligent control of a joint station, the automatic identification of abnormal conditions and automatic adjustment of operation schemes need to judge the running state of the system. In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) is proposed to optimize the Backpropagation Neural Network (BP) model (PSO-GWO-BP) and a pressure drop prediction model for the joint station export system is established using PSO-GWO-BP. Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy.
View Article and Find Full Text PDFOptimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework.
PLoS One
January 2025
Department of Information Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational results, such as student demographics, academic behaviours, and emotional health. This study presents the GNN-Transformer-InceptionNet (GNN-TINet) model to overcome the constraints of prior models that fail to effectively capture intricate interactions in multi-label contexts, where students may display numerous performance categories concurrently.
View Article and Find Full Text PDFRobust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets.
Front Artif Intell
January 2025
Department of Computer and Automatic Control, Faculty of Engineering, Tanta University, Tanta, Egypt.
Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.
Methods: A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed.
Data-driven discovery of associations between prescribed drugs and dementia risk: A systematic review.
Alzheimers Dement (N Y)
January 2025
Department of Health and Community Sciences, Medical School University of Exeter Exeter UK.
Abstract: Recent clinical trials on slowing dementia progression have led to renewed focus on finding safer, more effective treatments. One approach to identify plausible candidates is to assess whether existing medications for other conditions may affect dementia risk. We conducted a systematic review to identify studies adopting a data-driven approach to investigate the association between a wide range of prescribed medications and dementia risk.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!