Inverse system decoupling control strategy of BLIM based on stator flux orientation

Bu, Wenshao; Lu, Chunxiao; Zu, Conglin; Zhang, Haitao; Xiao, Jian

doi:10.3233/jae-140202

Cited by 22 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neglecting the mutual inductance coupling caused by the rotor eccentricity, the controllable magnetic suspension force components along the α ‐ and β ‐coordinate axes can be expressed as follows :

\begin{matrix} center \\ F_{α} = K_{normalm} (i_{s 2 d} ψ_{1 d} + i_{s 2 q} ψ_{1 q}) \\ F_{β} = K_{normalm} (i_{s 2 d} ψ_{1 q} - i_{s 2 q} ψ_{1 d}) \end{matrix}\}

where ψ 1 d and ψ 1 q are the air gap flux‐linkage components along the d‐ and q‐ coordinate axes; i s2 d and i s2 q are the suspension control current components along the d‐ and q‐ coordinate axes; and K m is the stiffness coefficient of controllable suspension force, which can be expressed as follows :

K_{normalm} = \frac{π L_{m 2}}{4 μ_{0} {italiclrW}_{1} W_{2}}

…”

Section: Mathematical Model Of Blimmentioning

confidence: 99%

“…The inverse system method is a feedback linearization decoupling method applied to a complex nonlinear system . There has been some research progress on the inverse system decoupling of the BLIM system . But in these studies, in order to simplify the state equation, the current dynamics of the stator winding is ignored, and therefore the dynamic decoupling performance is limited to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with that of the rotor flux linkage, the estimation accuracy of the stator flux linage is not affected by the rotor parameters. The inverse system decoupling method of the BLIM system based on the stator flux orientation (SFO) was studied in , but in the process of inverse system modeling the current dynamics of the torque winding was ignored. Meanwhile, there exists an unpredictable load torque variable in the inverse system model; this limits the improvement of the dynamic decoupling performance of the BLIM system and increases the implementation difficulty of the inverse system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stator flux orientation inverse system decoupling control strategy of bearingless induction motor considering stator current dynamics

Chen

2018

IEEJ Transactions Elec Engng

Self Cite

View full text Add to dashboard Cite

The bearingless induction motor (BLIM), is a multi-variable, nonlinear, and strongly coupled system. In order to improve its dynamic control performance, considering the stator current dynamics, a stator flux orientation (SFO) inverse system decoupling control strategy is proposed in this paper. Taking the dynamic characteristics of the torque winding current into account, the state equation of the BLIM system based on SFO is established. Then, on the basis of invertibility analysis, the inverse system dynamic mathematical model of the BLIM system is derived, the inverse system dynamic decoupling control strategy is presented, and the comprehensive design of the closed loop for each decoupled linear subsystem is carried out. Simulation experimental results show that the BLIM control system has excellent dynamic and static decoupling control performance, the influence of rotor parameters can be effectively overcome, and the ability to resist the load torque disturbance can be obviously improved.

show abstract

\begin{matrix} center \\ F_{α} = K_{normalm} (i_{s 2 d} ψ_{1 d} + i_{s 2 q} ψ_{1 q}) \\ F_{β} = K_{normalm} (i_{s 2 d} ψ_{1 q} - i_{s 2 q} ψ_{1 d}) \end{matrix}\}

K_{normalm} = \frac{π L_{m 2}}{4 μ_{0} {italiclrW}_{1} W_{2}}

…”

Section: Mathematical Model Of Blimmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stator flux orientation inverse system decoupling control strategy of bearingless induction motor considering stator current dynamics

Chen

2018

IEEJ Transactions Elec Engng

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bearingless technology is applicable for all kinds of AC motors [12,17,20,21,27]. Because of the robust and compact structure, the bearingless induction motor has become a hot research topic [18,20], [26][27][28][29][30][31][32], but there exists a complicated electromagnetic coupling in the bearingless induction motor [30,31]. To achieve its high performance control, it is necessary to realize the dynamic decoupling between different variables.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode variable structure control strategy of bearingless induction motor based on inverse system decoupling

Zhang

2018

IEEJ Transactions Elec Engng

Self Cite

View full text Add to dashboard Cite

A bearingless induction motor is a multivariable, nonlinear and strong coupling object. To achieve its dynamic decoupling control with high performance and improve the system stability, a sliding mode variable structure (SMVS) control strategy based on inverse system decoupling is proposed. First, taking the stator current as the control variable, by means of inverse system method, the bearingless induction motor system is decoupled into four pseudolinear subsystems, including motor speed subsystem, rotor flux‐linkage subsystem and two radial displacement component subsystems. Then, according to the SMVS control theory, the regulator is designed for each subsystem. To overcome the chattering problem of the system, an exponential reaching law is introduced to the SMVS regulator; based on Lyapunov theory, system stability is analyzed. From the simulation experimental results, it is clear that the system has better dynamic decoupling control performance, a stronger ability to resist load disturbance and stronger robustness to the variation of motor parameters. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

“…But the inverse system model is hard to accurately obtain in engineering practice. [13][14][15] Using the nonlinear approximation ability of neural network, neural network inverse system method can identify BIM inverse model and solve the tough problem of obtaining the accurate inverse system model, but the neural network's own defects (local minimum, complex computation, over learning, structure type choice over relying on experience, etc.) limit the further application of the neural network inverse system method.…”

Section: Introductionmentioning

confidence: 99%

Decoupling control of a 5-degree-of-freedom bearingless induction motor based on least squares support vector machine inverse

Wan

Yang

Sun

et al. 2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

In order to realize the nonlinear dynamic decoupling control of a 5-degree-of-freedom bearingless induction motor which consists of a 3-degree-of-freedom magnetic bearing and a 2-degree-of-freedom bearingless induction motor, a decoupling control strategy based on least squares support vector machine inverse is proposed. First, on the basis of introducing the structure of 5-degree-of-freedom bearingless induction motor, the mathematical model is derived. At the same time, the reversibility of the mathematical model is analyzed. Second, the inverse model of 5-degree-of-freedom bearingless induction motor is identified using the regress capability for high-dimensional nonlinear least squares support vector machine within limited samples. In addition, the particle swarm optimization algorithm is used to optimize parameters of the least squares support vector machine, which improves the fitting and predictive precision of the model. Third, combining the least squares support vector machine inverse model with the original system constitutes the pseudo linear system and then proportional-integral-derivative closed-loop controllers are designed to realize the compound control for the 5-degree-of-freedom bearingless induction motor system. The dynamic decoupling control among the radial and axial displacements, speed, and flux linkage is achieved. The simulation and preliminary experiment results all verify the effectiveness of the proposed control strategy.Keywords 5-Degree-of-freedom bearingless induction motor, magnetic bearing, least squares support vector machine inverse, inverse model, decoupling control Date

show abstract

Inverse system decoupling control strategy of BLIM based on stator flux orientation

Cited by 22 publications

References 17 publications

Stator flux orientation inverse system decoupling control strategy of bearingless induction motor considering stator current dynamics

Stator flux orientation inverse system decoupling control strategy of bearingless induction motor considering stator current dynamics

Sliding mode variable structure control strategy of bearingless induction motor based on inverse system decoupling

Decoupling control of a 5-degree-of-freedom bearingless induction motor based on least squares support vector machine inverse

Contact Info

Product

Resources

About