An Enhanced Approach for Parameter Estimation: Using Immune Dynamic Learning Swarm Optimization Based on Multicore Architecture

The identification of physical parameters is crucial for control system designs, condition monitoring and fault diagnosis of industrial drive systems. The article brings multicore architecture based parallel computing technology and bioinspired intelligent optimisation algorithm insight into designing for system parameter estimation models.models. In this study, a parallel implementation using an immune-cooperative dynamic learning particle swarm optimization (PSO) algorithm with multicore computation architectures is presented for permanent magnet synchronous machine (PMSM) parameter estimations. Three novel strategies are discussed, all with the purpose of enhancing the dynamic response and fast convergence performance of the designed parameter estimator. The strategies include a dynamic velocity modification strategy, an immune-memory-based searched information preservation mechanism, and an immune-network-based learning operator for PSO. Finally, a proposed method is applied to the parameter estimations of PMSMs as well as parallel running on multicore central processing units (CPU).