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A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation

Activity: Talk or presentation › Conference Presentations › Research

Paolo Mercorelli - Speaker

Participation in the "Proceedings of the 2nd International Conference on Applied Physics, System Science and Computers, APSAC2017" and presentation of the following contribution: "A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation"
27.09.2017 → 29.09.2017

Event

International Conference on: Applied Physics, System Science and Computers 2017

27.09.17 → 29.09.17

Dubrovnik, Croatia

Event: Conference

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Activity: Talk or presentation › Guest lectures › Education

Links

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Publications

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  10. Applying Bayesian Parameter Estimation to A/B Tests in e-Business Applications
  11. Inversion of fuzzy neural networks for the reduction of noise in the control loop
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  13. Enhancing Performance of Level System Modeling with Pseudo-Random Signals
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  15. Using Complexity Metrics to Assess Silent Reading Fluency
  16. Using Local and Global Self-Evaluations to Predict Students' Problem Solving Behaviour
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  19. Continuous 3D scanning mode using servomotors instead of stepping motors in dynamic laser triangulation
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  22. A coding scheme to analyse global text processing in computer supported collaborative learning: What eye movements can tell us
  23. Tracing exploratory modes in digital collections of museum Web sites using reverse information architecture
  24. Using the flatness of DC-Drives to emulate a generator for a decoupled MPC using a geometric approach for motion control in Robotino
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  26. Methodologies for Noise and Gross Error Detection using Univariate Signal-Based Approaches in Industrial Application
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  28. Classical PI Controllers with Anti-Windup Techniques Applied on Level Systems
  29. Analysis and comparison of two finite element algorithms for dislocation density based crystal plasticity
  30. Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications
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  32. Introducing parametric uncertainty into a nonlinear friction model
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