Systems, Man & Cybernetics - January 2016 - 32

developed for the optimization of parameter estimations.
More importantly, the computation time of PICDPSO-M
is only 12.932 s, which is the smallest computation time
among the seven hybrid PSO algorithms. It is evident
that PICDPSO-M proved to be effective for identifying
multiple parameters for the PMSM, and the stability of
the identification curve is improved when compared
to other PSOs, which is due to dynamic velocity modification strategy, an immune-memory-based searched
information preser vation mechanism, an immunenetwork-based learning mechanism, and multicore parallel computing technology.
As shown in Figure 7, the estimated winding resistance (0.3712 Ω) agrees well with the measurement
value (0.373  Ω) in a normal condition (with measurement data temperature under 25°C). It is obvious that
the estimated parameter values are quite close to the
nominal values listed in Table 1 under normal temperature. The proposed PICDPSO-M is of high precision, and
the estimated parameters such as motor resistance, dqaxis inductances, and the rotor flux are converging to
their right points rapidly. In Figure 7, the four plots
appear relatively stable compared to other hybrid PSOs,
which means that the proposed parameter estimator is
feasible with dynamic response and robust and fast convergence performance. A speed-up ratio [(SUR) = Ts/Tp]
is used to evaluate the computing performance of
PICDPSO-M as it runs on multicore architecture, where
Ts and Tp are the sequential and parallel execution
runs, respectively. From the results of Figure 8, as we
can see, the execution runs of PICDPSO-M greatly
decrease with the increased number of CPU cores. For
example, results show that the average time of 125.187 s,
87.143 s, 78.937 s, and 61.854 s are required for CPUs
with one core, two cores, three cores, and four cores,
respectively. As can be seen in Figure 8, it is clear that
the SUR is greatly improved with the processors
increasing. For example, tests indicate that SUR of one
core, two cores, three cores, and four cores have 1, 1.34,
1.59, and 2.02, respectively. It is clear that the SUR is
increasingly improved by the multicore architecture,
and it achieves linear speed up depending on the number of cores used. Furthermore, the proposed method

can be scaled to a large multicore computing system
with the identified system scale increased.
Conclusion
It is crucial for estimations of parameters and operation
states in industrial drive systems to be obtained accurately, quickly, and at low cost. A novel parameter estimation approach for PMSM using immune dynamic
learning particles and swarm optimization algorithms
based on multicore parallel architecture is proposed. To
enhance the dynamic response performance and fast
convergence performance of the designed parameter
estimator, several novel strategies were investigated to
improve the performance of the PSO, such as a dynamic
velocity modification strategy, an immune-memorybased searched information preservation mechanism,
and an immune-network-based learning operator for
swarms in the proposed PICDPSO-M. Furthermore, the
computational efficiency of the proposed method is
greatly enhanced by using multicore architecture with
parallel computation techniques. Finally, the PMSM
parameter estimation experiments prove that the proposed method is effective in tracking the machine electromagnetic parameters involving dq-axis inductances,
stator winding resistance, and rotor flux linkage. All in
all, the proposed parameter estimation method is costefficient, does not require extra sensors to measure
extra machine parameters, and is smart-it is fast
enough to enable online execution with the use of multiple CPU cores. The proposed method is a generic model
that can be applied to other industrial parameter identification systems and can assist in operation prediction
and state observation of practical systems. In our future
work, we will apply the proposed parameter estimation
method to aid condition monitoring, fault diagnosis, and
immeasurable mechanical parameter estimation in practical industrial PMSM drive systems.
Acknowledgments
This work was supported in part by the National Natural
Science Foundation of China under Grant (51374107,
61503134, 51577057, 61573299), the China Postdoctoral Science Foundation funded project under Grant (2013M540628,
2014T70767), and the Hunan Provincial Education Department outstanding youth project under Grant (15B087).

2.5
SUR

2
1.5
1
0.5
0

CPU-1

CPU-2

CPU-3

CPU-4

Figure 8. the SUr of PICDPSO-m on multicore

architecture.
32

IEEE SyStEmS, man, & CybErnEtICS magazInE Janu ar y 2016

About the Authors
Zhao-Hua Liu (zhaohualiu2009@hotmail.com) is a lecturer with the School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan,
China. He earned his M.Sc. degree in computer science and
technology and his Ph.D. degree in electrical engineering
from Hunan University, Changsha, China, in 2010 and 2012,
respectively. He worked as a visiting researcher in the
Department of Automatic Control and Systems Engineering
at the University of Sheffield, United Kingdom, from 2015


http://www.M.Sc

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