Optimizing piezoelectric actuator placement for enhanced vibration control using genetic algorithms

Abstract

Active vibration control in flexible structures remains a critical challenge in engineering applications. This study proposes an optimization framework for piezoelectric sensor and actuator configurations in cantilever beams using a genetic algorithm. An objective function based on controllability and observability criteria was formulated, integrating modal strain and natural frequency analyses. A small-habitat genetic algorithm was employed to determine optimal sensor/actuator positions, validated through frequency- and time-domain simulations. Comparative experiments with three alternative control methods demonstrated improved vibration suppression, achieving amplitude rejection rates as low as 1.2% under random and sinusoidal excitations. The method was further extended to a vehicle suspension model, where the proposed system achieved near-zero suspension dynamic travel under step inputs, outperforming the other three control methods in terms of vibration suppression effectiveness. Results highlight the framework's adaptability for enhancing structural resilience in aerospace, automotive, and robotic systems, providing a systematic approach for optimizing smart material configurations in vibration-sensitive applications.