Model Predictive Vibration Control [electronic resource] : Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures /
Takács, Gergely.author.
Rohaľ-Ilkiv, Boris.author.
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. .
Description based upon print version of record.
Includes bibliographical references and index.
1. Introduction -- 2. Basics of Vibration Dynamics -- 3. Smart Materials in Active Vibration Control -- 4. Algorithms in Active Vibration Control -- 5. Laboratory Demonstration Hardware for AVC -- 6. Basic MPC Formulation -- 7. Stability and Feasibility of MPC -- 8. Efficient MPC Algorithms -- 9. Applications of Model Predictive Vibration Control -- 10. MPC Implementation for Vibration Control -- 11. Simulation Study of Model Predictive Vibration Control -- 12. Experimental Model Predictive Vibration Control -- A. FE Modeling of the Active Structure -- B. MPC Code Implementation Details.
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. .
Control engineering.
Mathematical models.
Vibration.
Dynamical systems.
Dynamics.
Computational intelligence.
Computer mathematics.
System theory.
Control and Systems Theory.
Mathematical Modeling and Industrial Mathematics.
Vibration, Dynamical Systems, Control.
Computational Intelligence.
Computational Mathematics and Numerical Analysis.
Systems Theory, Control.