Systems, Man & Cybernetics - January 2016 - 27

central processing units (CPUs). The results illustrate that
the proposed method can effectively estimate multiple
parameters of PMSMs. Moreover, the computational efficiency of the proposed method is greatly enhanced by
using multicore parallel computation techniques, which
satisfies the required real-time response needed in drive
control systems. The proposed method is a generic model
that can be applied to other nonlinear parameter identification systems and can assist in operation prediction and
state observation of a system.
In recent years, PMSMs have been widely used in
industrial drive systems, like those used for electric automobiles, aerospace/aviation, and wind power generation
[1]-[3], since they require low energy consumption, lower
maintenance cost, and fast torque response. However, it
is important to obtain electromagnetic parameters accurately for control system designation, condition monitoring, and fault diagnosis of the PMSMs [2]. Traditionally,
these system parameters such as stator winding resistance, inductances, and rotor flux linkage are measured
by using additional instruments, which increases expensive laboratory equipment investment. In reality, it is not
convenient to measure the machine parameters by using
measurement equipment, especially in remote areas. System identification theory is an ideal technology for estimating PMSM parameters, as it only needs regular signal
input for the estimator. In recent years, many parameter
estimation methods have been developed for PMSMs,
such as the extended Kalman filter (EKF) [4]-[6], model
reference adaptive systems (MRASs) [7], [8], recursive
least-square (RLS) methods [9]-[10], and adaptive observers [11], and these are widely used for estimating desired
parameters. However, the disadvantages are apparent
since the existing EKF and RLS estimators suffer from
unidentified PMSM parameters, and the EKF and RLS
methods are sensitive to noise [4], [9]. As explained in [7]
and [8], the MRASs are designed to estimate each parameter separately by fixing some parameters to their nominal values first and then estimating another parameter. In
reality, machine electromagnetic parameters should be
estimated simultaneously since the estimations interrelate. An adaptive interconnected observer that achieves
an accurate PMSM parameter observation [11] was investigated, but it may easily suffer from parametric uncertainties since it does not consider the dynamics of a
PMSM. Thus, adaptive intelligent dynamic parameter
estimation methods need to be developed [12].
Recently, biologically inspired computing methods
have attracted much attention in the investigation of
PMSM parameter identification [13]. The system identification problem can be treated as a gray-box model-based
parameter optimization method. Particularly, PSO algorithms have been introduced as an attractive optimization
technique to be used for parameter estimation because
PSO has the advantages of easier implementation, good
performance, and fast convergence speed when handling

multiple parameter optimization problems [14]. However,
the convergence performance and dynamic track performance are big challenges for PSO when dealing with the
practical engineering problem. There are two reasons for
this. First, the PSO will suffer from a few inherent disadvantages such as, for example, search stagnation during
the later evolution stage. Second, a significant amount of
computation time is required for the PSO as it requires a
large number of iteration calculations, especially on a traditional single central processor.
To achieve the faster dynamic response required for
PMSM parameter estimations, there are two important
issues that need to be solved for the PSO. These are
dynamic solution performance and fast convergence performance. A dynamic velocity modification strategy can
increase the activity of particles. An immune system
mechanism can activate the dynamic performance during
the search process [13]. Multicore processor techniques
have been widely used in personal computers and highperformance computing systems, which has made parallel
processing convenient and economical in recent years
[15]. This article aims to bring multicore-architecturebased parallel computation technology and bioinspired
intelligent optimization algorithm insight into the design
of parameter estimation models for PMSMs. In this article, a multicore-architecture-based parallel intelligent
parameter estimator that combines an immune learning
mechanism, PSO, and parallel computing for PMSM
parameter estimation is proposed. This proposed estimation method will be referred to as PICDPSO-M. In PICDPSO-M, three novel strategies are implemented to enhance
the solution performance of the PSO. First, a dynamic
velocity modification strategy is designed to enhance the
activity of particles; second, the immune memory is used
to conserve searched information and is enhanced by a
designed immune comprehensive learning strategy
(ICLS); and third, an immune-network-based learning
operator is employed to accelerate the convergence speed
of Pbests particles. Furthermore, parallelization of PICDPSO-M is conducted using a multicore-architecture computer. Finally, the proposed method is used for the
parameter estimation of PMSMs, which shows that the
proposed estimator has better performance when estimating multiple electromagnetic parameters simultaneously. Additionally, the results show that the proposed
approach possesses high speed up and a considerable time
cost. The proposed method can also be applied to other
industrial system parameter identification state observers.
PMSM Model and Parameter
Estimation Modeling
PMSM Model
The dq-axis voltage equations of the PMSM are usually
employed for the parameter estimation of the PMSM [3];
the model of the PMSM is shown in the following:
Ja nu a r y 2016

IEEE SyStEmS, man, & CybErnEtICS magazInE

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Table of Contents for the Digital Edition of Systems, Man & Cybernetics - January 2016

Systems, Man & Cybernetics - January 2016 - Cover1
Systems, Man & Cybernetics - January 2016 - Cover2
Systems, Man & Cybernetics - January 2016 - 1
Systems, Man & Cybernetics - January 2016 - 2
Systems, Man & Cybernetics - January 2016 - 3
Systems, Man & Cybernetics - January 2016 - 4
Systems, Man & Cybernetics - January 2016 - 5
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Systems, Man & Cybernetics - January 2016 - 12
Systems, Man & Cybernetics - January 2016 - 13
Systems, Man & Cybernetics - January 2016 - 14
Systems, Man & Cybernetics - January 2016 - 15
Systems, Man & Cybernetics - January 2016 - 16
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Systems, Man & Cybernetics - January 2016 - Cover3
Systems, Man & Cybernetics - January 2016 - Cover4
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