Parameter identification of hysteresis model of macro-micro composite actuator based on improved gray wolf algorithm

The accurate identification of hysteresis model parameters is the key to ensuring the displacement tracking accuracy of macro-micro composite actuators. To address the shortcomings of traditional grey wolf algorithm (GWO), which is prone to local optima and premature convergence, an improved grey wolf algorithm (TGWO) is proposed. Updating the initial position of gray wolf individuals based on Singer chaotic mapping to improve population diversity; Adopting a nonlinear convergence factor strategy to improve local development and global search performance; Introducing dynamic weight updating and adaptive updating strategies in the iterative updating of population positions. Simulation and experiments have shown that this algorithm can effectively and reliably identify the parameters of the hysteresis model of macro-micro composite actuators, The mean relative error is 4.6%, with higher accuracy and convergence.