Dirk Soeffker

Professor

Universität Duisburg-Essen

Germany

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Dirk Soeffker is a Professor at the University of Duisburg-Essen, Germany. His research areas include forecasting models for energy production in wind and photovoltaic systems, adaptive control strategies for complex systems, and behavior prediction in driving and battery management. His recent publications focus on topics such as adaptive robust control for wind turbines, lane changing behavior prediction, and real-time power management in hybrid electric vehicles.

Articles (15)

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (1)

Neelu Madan

Aalborg University

DENMARK

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Dirk Soeffker

Research Interests

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Articles (15)

Modeling and Evaluation of Forecasting Models for Energy Production in Wind and Photovoltaic Systems

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

Multi-Level Feature Extraction and Classification for Lane Changing Behavior Prediction and POD-Based Evaluation

Adaptive Robust Observer-Based Control for Structural Load Mitigation and Speed Regulation in Commercial Wind Turbines

Quantification of human behavior levels by extending Rasmussen's SRK model and the effects of time pressure and training on the levels switching

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

Intelligent Real-Time Power Management of Multi-Source HEVs Based on Driving State Recognition and Offline Optimization

State Machine-Based Approach for Estimating the Capacity Loss of Lithium-Ion Batteries

Fast and Precise Generic Model for Position-Based Trajectory Prediction of Inland Waterway Vessels

A Review of HMM-Based Approaches of Driving Behaviors Recognition and Prediction

How to evaluate classifier performance in the presence of additional effects: A new POD-based approach allowing certification of machine learning approaches

New Metric for Evaluation of Deep Neural Network Applied in Vision-Based Systems

Wind turbine robust disturbance accommodating control using non‐smooth H∞ optimization

A Study on a HMM-Based State Machine Approach for Lane Changing Behavior Recognition

Determination of Takeover Time Budget Based on Analysis of Driver Behavior

Collaborators (1)

Neelu Madan

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