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Abstract:
This paper presents a novel predictive control strategy for the Adaptive Tuned Particle Impact Damper (ATPID), aimed at improving vibration suppression in systems with limited available information. The proposed control algorithm, called the Predictive Control Algorithm (PCA), is based on the prediction and optimization of system dynamics and operates effectively even when system parameters and external excitations are unknown. The only available input for the control process is the measured vibration response of the system. Under this constraint, the PCA accurately estimates the optimal height of the damper in real time, achieving vibration reduction of up to 75%. The algorithm also exhibits a second operational mode: when a theoretical model of the mechanical system is available, the PCA can incorporate this additional knowledge to further enhance control accuracy and performance. The algorithm’s two operating approaches enable its application in a wide range of engineering environments. The robustness of the approach is further validated through sensitivity analyses investigating the impact of variations in particle mass, excitation amplitude, and gravitational conditions. The results obtained from the PCA algorithm show that the height prediction error remains below 10%, with accuracy
increasing in conditions of higher excitation and particle mass. The main novelty of this work is the development of a versatile and fully adaptive predictive control algorithm for ATPID systems, capable of optimizing damper parameters based solely on vibration feedback but also leveraging mathematical system models when available. The proposed control algorithm represents important progress in the development of adaptive mechanical structures employing particle impact damping technology.

Keywords:
Mechanical vibrations, Adaptive damping, Adaptive Tuned Particle Impact Damper, Experimental and numerical investigations, Contact forces, Predictive control algorithm