Intelligent Parametric Identification of Flexible Manipulator System
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This paper presents an investigation into the development of an intelligence parametric identification for a single-link flexible manipulator system using genetic algorithm (GA) and particle swarm optimization (PSO). The global search technique of GA and PSO employ different strategies in the area of algorithm design, natural basis and computational effort in order to find the solution of a given objective function. A simulation environment characterizing the dynamic behavior of flexible manipulator system was first developed using finite difference (FD) method. The flexible manipulator was driven by a bang-bang torque and the input-output data of the system acquired is used for system identification using GA and PSO techniques based on autoregressive model structure. The identification is performed on basis of minimizing the mean-squared error (MSE) between the measured and estimated outputs of the flexible manipulator. The validation of the algorithm is assessed with correlation tests and in time and frequency domains. It is demonstrated that the PSO has perform far better with the value of MSE is 3.0147×10-6 compared to GA with 1.4908×10-4. The best model obtained characterizes the dynamic behavior of the system well and will be used in the future for control design and development
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