Decoupling Control of the 5 Degree-of-Freedom Bearingless Induction Motor Based on ¿-th Order Inverse System Method

Liu, Xianxing; Dong, Lei; Wenjin, Fan; Sun, Yanchao

doi:10.1109/chicc.2006.4346882

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The definition of a-order inverse system is as follows 28 Assuming system Q a is a q-input p-output system with the mapping relationship u = u a u, where u(t) = ½u 1 , u 2 , Á Á Á , u q T is an arbitrary given differentiable function vector and its initial value is satisfied with the initial conditions of the system S. Defining u i as the a i -order derivative of y di , namely u(t) = y d (a) (t), a = ½a 1 , a 2 , Á Á Á , a q T , if the operator u a is contented with the following equation, the system Q a can be called the a-order inverse system of the original system S uu a u = uu a y d a ð Þ = uu = y d ð17Þ…”

Section: Decoupling Of Integrated Chassis Systemmentioning

confidence: 99%

Decoupling control of chassis integrated system for electric wheel vehicle

Gao

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

In order to eliminate the mutual interferences among the differential assist steering system, the active suspension system and the electrical stability control, this paper proposes a sliding mode decoupling control strategy based on the inverse system method for the electric wheel vehicle. The dynamic model of the integrated chassis system is established, and the coupling relationship among the subsystems is analyzed by the correlation analysis. Based on the inverse system method, a compound pseudo linear system is constructed by an inverse system connected in series before the original chassis system, which is linearized and decoupled into three independent linear integral systems. In order to improve the robustness and anti-interference of the decoupled system, a pre-compensation controller based on the sliding mode control is designed for the pseudo linear system. The results of simulation and vehicle test show that the proposed decoupled controller has excellent decoupling performance, which can accomplish the single-channel control of the three decoupled subsystems, and eliminate their influences and interferences. Furthermore, it can effectively track the reference signal and reduce the impact of the external interference, which can obtain an excellent comprehensive performance of the chassis system.

show abstract

Section: Decoupling Of Integrated Chassis Systemmentioning

confidence: 99%

Decoupling control of chassis integrated system for electric wheel vehicle

Gao

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

Fuzzy neural network-based model reference adaptive inverse control for induction machines

Shao

Zhan

Guo

2009

2009 International Conference on Applied Superconductivity and Electromagnetic Devices

View full text Add to dashboard Cite

Conference on Applied Superconductivity and Electromagnetic Devices, This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Technology, Sydney's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Abstract-In this paper, because the induction machines are described as the plants of highly nonlinear and parameters timevarying, in order to obtain a very well control performances that a conventional model reference adaptive inverse control (MRAIC) can not be gotten, a fuzzy neural network-based model reference adaptive inverse control strategy for induction motors is presented based on the rotor field oriented motion model of induction machines. The fuzzy neural network control (FNNC) is incorporated into the model reference adaptive control (MRAC), a fuzzy basis function network controller (FBNC) and a fuzzy neural network identifier (FNNI) for asynchronous motors adjustable speed system are designed. The proposed controller for asynchronous machines resolves the shortage of MRAC, and employs the advantages of FNNC and MRAC. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness. Index Terms-induction machine, machine dynamic model, fuzzy neural network control (FNNC), model reference adaptive control (MRAC)I.

show abstract

Support vector machine-based fuzzy self-learning control for induction machines

Shao

2010

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering

View full text Add to dashboard Cite

Decoupling Control of the 5 Degree-of-Freedom Bearingless Induction Motor Based on ¿-th Order Inverse System Method

Cited by 5 publications

References 0 publications

Decoupling control of chassis integrated system for electric wheel vehicle

Decoupling control of chassis integrated system for electric wheel vehicle

Fuzzy neural network-based model reference adaptive inverse control for induction machines

Support vector machine-based fuzzy self-learning control for induction machines

Contact Info

Product

Resources

About