Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM

In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE

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)

GPU-accelerated parallel coevolutionary algorithm for parameters identification and temperature monitoring in permanent magnet synchronous machines

A hierarchical fast parallel co-evolutionary immune particle swarm optimization (PSO) algorithm, accelerated by graphics processing unit (GPU) technique (G-PCIPSO), is proposed for multiparameter identification and temperature monitoring of permanent magnet synchronous machines (PMSM). It is composed of two levels and is developed based on compute unified device architecture (CUDA). In G-PCIPSO, the antibodies (Abs) of higher level memory are selected from the lower level swarms and improved by immune clonal-selection operator. The search information exchanges between swarms using the memory-based sharing mechanism. Moreover, an immune vaccine-enhanced operator is proposed to lead the Pbests particles to unexplored areas. Optimized parallel implementations of G-PCIPSO algorithm is developed on GPU using CUDA, which significantly speeds up the search process. Finally, the proposed algorithm is applied to multiple parameters identification and temperature monitoring of PMSM. It can track parameter variation and achieve temperature monitoring online effectively. Compared with a CPU-based serial execution, the computational efficiency is greatly enhanced by GPU-accelerated parallel computing technique

Leonuketal, a Spiroketal Diterpenoid from <i>Leonurus japonicus</i>

An architecturally complex spiroketal diterpenoid, leonuketal (<b>1</b>), was isolated from the aerial parts of the plant <i>Leonurus japonicus</i>. This compound possessed an unprecedented tetracyclic skeleton that comprised a bridged spiroketal moiety fused with a ketal-γ-lactone unit. The structure and absolute configuration were determined by spectroscopic analyses, a modified Mosher’s method, and ECD (electronic circular dichroism) calculations. Leonuketal (<b>1</b>) showed significant vasorelaxant activity against KCl-induced contraction of rat aorta, with the EC₅₀ value of 2.32 μM