Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—In order to improve the control precision of the giant magnetostrictive actuator (GMA), the unknown parameters of the hysteresis nonlinear model are quickly identified based on the test data before use, and the GMA nonlinear dynamic model is established based on the free energy hysteresis model. Aiming at the shortcomings of standard particle swarm optimization (PSO) algorithm and the tendency to fall into local optimum in the late iteration, an improved chaotic particle swarm optimization algorithm with dynamic adjustment of flight time and optimal position of the group through chaotic traversal optimization is proposed. ICPSO), and the algorithm is applied to the parameter identification of actuator nonlinear model. Experiments show that the algorithm can identify GMA nonlinear dynamic model parameters with high efficiency, and the identified model can be well fitted with experimental data. The hysteresis displacement error is within 3%, and the kinetic model is highly reproducible by multiple comparisons.
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