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This paper presents the modeling and active vibration control using an evolutionary swarm algorithm known as particle swarm optimization. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges as boundary conditions constrained at horizontal position. The purpose of the experimental rig development is to collect the input-output vibration data. Next, the data acquisition and instrumentation system were designed and integrated with the experimental rig. Several procedures were conducted to acquire the input-output vibration data. The collected vibration data were then utilized to develop the system model. The parametric modeling using particle swarm optimization was devised using an auto regressive model with exogenous model structure. The developed model was validated using mean square error, one step ahead prediction, correlation tests and pole-zero diagram stability. Then, the developed model was used for the development of controller using an active vibration control technique. It was found that particle swarm optimization based on the active vibration control using Ziegler-Nichols method has successfully suppressed the unwanted vibration of the horizontal flexible plate system. The developed controller achieved the highest attenuation value at the first mode of vibration which is the dominant mode in the system with 34.37 dB attenuation.

This paper presents an investigation of system identification using parametric modeling approaches for a singlelink flexible manipulator system. The utilization of a particle swarm optimization (PSO) technique for modeling of a highly non-linear system is studied in comparison to the conventional recursive least squares (RLS) technique. A simulation environment characterizing the dynamic behavior of the flexible manipulator system was first developed using finite difference (FD) approach to acquire the input-output data of the system. A bang-bang torque was applied as an input and the dynamic response of the system was investigated. A comparative assessment of the two models in characterizing the manipulator system is presented in time and frequency domains. Results demonstrate the advantages of PSO over RLS in parametric modeling. The developed model achieved will be used for control design and development in future work.

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