Parameter identification of engineering problems using a differential shuffled complex evolution

Pourasghar, Babak; Ahandani, Morteza Alinia; Kharrati, Hamed

doi:10.1007/s10462-019-09745-0

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…In reference [23][24][25], differential evolution algorithm and particle swarm optimization algorithm are combined and applied to parameter identification in different backgrounds. The hybrid algorithm avoids the shortcomings of the two algorithms and has good accuracy and speed in identifying various parameters.…”

Section: Introductionmentioning

confidence: 99%

Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

Zhou¹,

Wang²,

Du³

et al. 2023

Computers, Materials &Amp; Continua

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The parameters of permanent magnet synchronous motor (PMSM) affect the performance of vector control servo system. Because of the complexity of nonlinear model of PMSM, it is very difficult to identify the parameters of PMSM. Aiming at the problems of large amount of data calculation, low identification accuracy and poor robustness in the process of multi parameter identification of permanent magnet synchronous motor, this paper proposes a weighted differential evolutionary particle swarm optimization algorithm based on double update strategy. By introducing adaptive judgment factor to control the proportion of weighted difference evolution (WDE) algorithm and particle swarm optimization (PSO) algorithm in each iteration process, and consider using PSO algorithm or WDE algorithm to update individuals according to the probability law. The individuals obtained from WDE operation are used to guide the individual evolution process in PSO operation through the information exchange mechanism. The proposed WDEPSO algorithm can ensure the diversity and effectiveness of the individual evolution of the population. The algorithm is applied to parameter identification of PMSM drive system. The simulation results show that the proposed algorithm has better convergence performance and has strong robustness, parameter identification of permanent magnet synchronous motor based on proposed method does not need to rely on more data sheet on the motor design value, can motor stator resistance identification at the same time, the rotor flux linkage, d/q-axis inductance and electrical parameters, and can effectively track the parameters value.

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Section: Introductionmentioning

confidence: 99%

Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

Zhou¹,

Wang²,

Du³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…However, this algorithm is only applicable to non-convex extreme PMSMs, and parameter identification of convex extreme PMSMs still needs further research. References [28]- [30] combined the differential evolution algorithm with the particle swarm optimization algorithm and applied them to parameter identification under different backgrounds. This hybrid algorithm avoids the shortcomings of the two algorithms and has good parameter identification accuracy, but the hybrid formula is complex, with many parameters, and the algorithm time complexity is high.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Permanent Magnet Synchronous Motor Based on CGCRAO Algorithm

Li,

Jian

2023

IEEE Access

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In response to the challenge of easily falling into local optima and slow identification speed in the parameter identification of permanent magnet synchronous motors (PMSMs), this paper proposes a CGCRAO algorithm based on chaotic initialization and a hybrid variation strategy. The algorithm uses Tent chaotic mapping for population initialization to improve population diversity. At the same time, by combining the Gaussian and Cauchy distribution characteristics and the three-stage operation idea, the optimal individual variation strategy is autonomously selected in real time to improve the RAO-1 algorithm. This paper validates the effectiveness of the algorithm improvement and the correctness of the threestage operation idea using eight benchmark test functions. Furthermore, This paper conducted comparative experiments on parameter identification of five algorithms under different operating conditions through simulation and experiments. The results indicate that the proposed CGCRAO algorithm enables fast and accurate identification of PMSM parameters.INDEX TERMS CGCRAO algorithm, permanent magnet synchronous motors (PMSMs), three-stage running idea, Chaos initialization, Gaussian-Cauchy hybrid variation.

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“…Global heuristic optimization methods are an active field of research with more models and strategies proposed every year. Some examples are genetic algorithms (GA) [5,6], differential evolution [7,8], particle swarm optimization (PSO) [9,10], the Big Bang-Big Crunch (BB-BC) algorithm [11], evolution strategy (ES) [12], simulated annealing [13], the Shuffled Complex Evolution (SCE-UA), and some variations of the SCE-UA model [14,15].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

et al. 2019

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The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.

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Parameter identification of engineering problems using a differential shuffled complex evolution

Cited by 6 publications

References 31 publications

Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

Parameter Identification of Permanent Magnet Synchronous Motor Based on CGCRAO Algorithm

Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

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