Contributions To Model Predictive Active Vibration Control Under Parametric Resonance
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Contributions to Model Predictive Active Vibration Control Under Parametric Resonance
This thesis addresses the problem of active vibration control in the presence of parametric resonance. Parametric resonance arises in a broad class of systems typically characterized by time-varying structures, such as stacker cranes with variable load changes. This thesis is divided mainly into two parts. In the first part, a mathematical and a multi-body model are developed and experimentally validated on a lab-scale prototype. In addition, modal analysis is carried out analytically and experimentally. The second part highlights the challenges that parametric resonance poses for control. For this purpose, three approaches are presented. One of the common features of these approaches is the use of nonlinear model predictive control (NMPC) for the predictive countermeasure to parametric resonance, mainly for optimal trajectory planning instead of conventional methods such as input shaping. Furthermore, all three approaches share insights from the modal analysis where the time propagation of the parametric resonance is predictable. In two approaches, trajectory planning can avoid critical frequencies such as resonance frequencies by involving them as soft boundary conditions. The third approach develops a promising and completely different concept of active vibration damping based on the idea of shaping the frequency spectrum of the state and the input. Unlike the usual time domain MPC formulation, this spetral shaping is formulated as an optimization problem defined in the frequency domain. In addition to computer simulations, a real-time implementation of the nonlinear model predictive vibration control is also performed on the test bench.
Optimal active power control of wind turbines for grid stability support
Author: Bashar Mousa Melhem
language: en
Publisher: Logos Verlag Berlin GmbH
Release Date: 2025-03-07
This dissertation addresses the critical challenge of grid frequency stability in the context of increasing reliance on renewable energy sources, particularly wind power. As the integration of wind turbines into power systems grows, ensuring their effective contribution to frequency regulation becomes essential. This research proposes a novel approach that employs data-enabled predictive control to enhance the frequency control of the future heterogeneous power grid and hence improve the overall grid stability. The study begins with a comprehensive analysis of the dynamic interactions between wind turbines and the power grid, identifying key factors that impact frequency control. A predictive control framework is developed to anticipate grid frequency fluctuations and optimize turbine responses. Through rigorous simulations and practical case studies, the author demonstrates the effectiveness of the proposed strategies in mitigating frequency deviations and improving overall system resilience. The findings highlight that data-enabled control not only enhances the responsiveness of wind turbines, among other generating units, to frequency support but also contributes significantly to a more stable and reliable power grid.
Model Predictive Vibration Control
Author: Gergely Takács
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-03-05
Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: · the implementation of computationally efficient algorithms · control strategies in simulation and experiment and · typical hardware requirements for piezoceramics actuated smart structures. The use of a simple laboratory model and inclusion of over 170 illustrations provides readers with clear and methodical explanations, making Model Predictive Vibration Control the ideal support material for graduates, researchers and industrial practitioners with an interest in efficient predictive control to be utilized in active vibration attenuation.