Speed control of drive-train incorporating magnetic coupling

Liao, Xiaowen; Bingham, Chris; Zolotas, Argyrios; Zhang, Qinghua; Smith, Tim

doi:10.1109/icem49940.2020.9270948

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Field engineers can refer to [20] for a simple parameter setting method. But this strategy does not specify the bandwidth of load-torque disturbance and speed reference that the controller can accommodate.…”

Section: H ∞ Mixed-sensitivity Methods Of Optimizing Pi Parametersmentioning

confidence: 99%

“…Using classical methods, designs to accommodate both disturbance rejection and reference tracking performance of a speed regulation system can be realized using a two-degree-offreedom proportional-integral (2DOF PI) controller [17]- [20]. The low-pass pre-filter of a 2DOF PI controller performs input shaping which can prevent significant excitation of resonant dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Servo Control of Drive-Trains Incorporating Magnetic Couplings

Liao

Bingham

Zolotas

et al. 2022

IEEE Trans. on Ind. Applicat.

Self Cite

View full text Add to dashboard Cite

The paper presents a high performance and low-cost design methodology for the servo control of magnetic drive-trains (MDTs) operating in direct drive mode. For the first time, this paper considers using sensitivity peaks to analyse the robustness and stability of MDT control systems. Initially, through analysis of a dynamic model, the key spring characteristic parameters with respect to operating points, are developed. It is also shown that a wider dynamic performance envelope can be achieved by linearizing the MDT model at around 60%-80% of the maximum coupling torque, as opposed to traditional linearization under zero torque conditions. Subsequently, the paper exploits the spring characteristics for a design methodology based on the are validated through simulation and experimental studies.

show abstract

Section: H ∞ Mixed-sensitivity Methods Of Optimizing Pi Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%