A nonlinear Wiener system identification based on improved adaptive step-size glowworm swarm optimization algorithm for three-dimensional elliptical vibration cutting

Three-dimensional elliptical vibratory cutting (3D EVC) is a precision machining technology that has been extensively used in the aerospace, military, and civil industries. However, if the best processing performance and precise control are required, system identification is the primary prerequisite. Therefore, a 3D EVC nonlinear Wiener model system identification is studied, and an improved adaptive step-size glowworm swarm optimization algorithm (IASGSO) is proposed to identify and optimize the nonlinear Wiener system model parameters. The step-size value of the IASGSO algorithm is adjusted by the distance based on the global best point, which overcomes the shortcomings of the traditional glowworm swarm optimization (GSO) algorithm that is easy to be premature and generate oscillation near the global optimal solution in the later period. The results show that the IASGSO algorithm has the advantage of effectively searching the global optimum value over the traditional GSO algorithm by using two standard test functions to test the performance of both the GSO algorithm and IASGSO algorithm. Based on the system identification experiment, the fitting rate of the proposed method can reach the accuracy of 98.46%, proving the proposed IASGSO algorithm feasibility and accuracy to a large extent. It is expected that both the proposed IASGSO algorithm and the findings in this work will not only solve the local convergence problem of traditional algorithms but also make sense for the precision machining of processing systems.