Do, M. H.; Söffker, D.: Wind Turbine Disturbance Accommodating Control Using H infinity Optimization. Wind Energy, June, 2021.[PDF][LINK]

Do, M. H.; Söffker, D.: State-of-the-Art in Integrated Prognostics and Health Management Control for Utility-Scale Wind Turbines. Renewable and Sustainable Energy Reviews, 2021.[PDF][LINK]

Spiller, M.; Söffker, D.: Stator-Rotor Contact Force Estimation of Rotating Machine Automation. Automation, Vol. 2, No. 3, 2021, pp. 83-97.[PDF][LINK]

Bakhshande, F.; Söffker, D.: What makes the difference? State-of-the-art and new perspectives on precision and task-oriented drones for agricultural use. AgEng2020 Conference, Portugal, 2021, accepted.

David, R.; Rothe, S.; Söffker, D.: Lane changing behavior recognition based on Artificial Neural Network-based State Machine approach. 2021 IEEE International Conference on Intelligent Transportation ( ITSC), Indianapolis, USA, 2021.[PDF][LINK]

He, C.; Lum, Y.; Lee, K.; Söffker, D.: Human reliability estimation based on fuzzy logic-modified CREAM approach. 2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Tallinn, Estonia, 2021, pp. 45-50.[PDF][LINK]

He, C.; Söffker, D.: Human reliability analysis in situated driving context considering human experience using a fuzzy-based clustering approach. 2nd IEEE International Conference on Human-Machine Systems, Magdeburg, Germany, 2021, pp. 8-10.[PDF][LINK]

Owino, L., Söffker, D.: Modeling and prediction of corn growth during vegetative phase. AgEng 2021 conference, Évora, 2021, pp. 426-432.[LINK]

Owino, L., Söffker, D.: State machine-based model for estimation and prediction of above ground biomass in corn during vegetative stage. AgEng 2021 conference, Évora, 2021, pp. 433-440.[LINK]

Owino, L.; Söffker, D.: Predictive precision irrigation-based control of maize leaf growth under laboratory conditions. Gesellschaft fur Pflanzenbauwissenschaften e.V, 63. Jahrestagung, Rostock, September 28-30, 2021, pp. 261-262.[LINK]

Spiller, M.; Söffker, D.: Robust Control of Relative Degree Two Systems Subject to Output Constraints with Time-Varying Bounds. 60th IEEE Conference on Decision and Control (CDC), Austin, Texas, 2021,  pp. 5402-5409.[PDF][LINK]

Wei, X.; Demmerling, A. L.; Söffker, D.: Metalworking Fluid Classification Based on Acoustic Emission Signals and Convolutional Neural Network. European Conference of the PHM Society, Vol. 6, No. 1, 2021, pp. 471-476.[PDF][LINK]

Bejaoui, A.; Bakhshande, F; Söffker, D.: Modeling and formalization of Human operator behavior for mobile systems and inland vessels using the Situation-Operator-Modeling approach. Autonomous Inland and Short Sea Shipping, Duisburg, 2021.[LINK]

Boschmann, W.; Söffker, D.: Increasing detection performance using redundant object detection approaches. Autonomous Inland and Short Sea Shipping, Duisburg, 2021.

Donandt, K.; Söffker, D.: Deep Learning-based Vessels Driving Behavior Prediction in Inland Navigation. Autonomous Inland and Short Sea Shipping Conference (AISS) 2021, Duisburg-Essen, Nov 2-3, 2021.[LINK]

Kipchirchir, E.; Do, M. H.; Njiri, J. G.; Söffker, D.: Adaptive robust observer-based structural load mitigation of wind turbines. Wind Energy Science Conference (WESC) 25-28 May 2021, Hannover, May 25-28, 2021.[LINK]

Kipchirchir, E.; Do, M. H.; Njiri, J. G.; Söffker, D.: Prognostics-based adaptive control strategy for lifetime control of wind turbines. Wind Energy Science Conference (WESC) 25-28 May 2021, Hannover, May 25-28, 2021.[PDF][LINK]

Mathew, J.; Rajput, R. R.; Söffker, D.: Optimizing hybrid powertrains using MATLAB & Simulink. MATLAB Day at University of Duisburg-Essen, Mathworks, invited talk, Apr 21, 2021.

Thind, N.S.; Spiller,M.; Söffker, D.: Data-driven prediction of inland vessel trajectories. Autonomous Inland and Short Sea Shipping Conference (AISS), Duisburg, Okt 15, 2021.