This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation.

本文介绍了一种主动振动控制的开发方法，该方法采用比例积分微分控制器，通过Ziegler-Nichols的传统方法和一种称为粒子群优化算法的智能方法，对水平柔性板结构的振动进行抑制。最初，设计和制造实验装置时将所有边缘夹紧在柔性板的水平位置。设计了数据采集和仪器系统，并将其集成到实验装置中，以收集柔性板的输入-输出振动数据。通过实验研究获得的振动数据被用于基于系统识别技术的系统建模，该系统基于具有外生输入结构的自回归系统。使用粒子群优化算法对板系统进行建模，并使用均方误差，提前一步预测和相关测试进行验证。使用零极点图稳定性评估模型的稳定性。粒子群优化算法的适应度函数定义为水平柔性板系统的测量输出与估计输出之间的均方误差。接下来，开发的模型用于主动振动控制的开发中，该振动控制使用比例积分微分控制器对水平柔性板系统进行振动抑制。比例-积分-微分增益可通过两种不同的方式来最佳确定：通过Ziegler-Nichols调整规则调整的常规方法和通过粒子群优化算法调整的智能方法。评估并验证了开发的控制器的性能。比例积分微分粒子群优化控制器通过实现47.28 dB的衰减实现了第一振动模式的最高衰减值，而比例积分微分的Ziegler-Nichols控制器则仅实现了34.21 dB的衰减。