2015 15th International Conference on Control, Automation and Systems (ICCAS) 2015
DOI: 10.1109/iccas.2015.7364794
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##### Cited by 8 publications
(6 citation statements)
##### References 15 publications
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“…In order to analyze the characteristics of the three phase nonlinear IPMSM for different operating temperatures, the most exclusive IPMSM model among [38][39][40][41][42][43][44][45] has been adopted. Saturation, coupling, spatial harmonics and core losses are assumed negligible in this nonlinear mathematical model.…”
Section: Ipmsm Mathematical Modellingmentioning
confidence: 99%
“…In order to analyze the characteristics of the three phase nonlinear IPMSM for different operating temperatures, the most exclusive IPMSM model among [38][39][40][41][42][43][44][45] has been adopted. Saturation, coupling, spatial harmonics and core losses are assumed negligible in this nonlinear mathematical model.…”
Section: Ipmsm Mathematical Modellingmentioning
confidence: 99%
“…The velocity tracking error which is included in the second state x 2 (t) = e w (t) is penalized by the weighting scalar σ and the control efforts u α (t), u β (t) are penalized by the weighting scalars η and μ, respectively. The choice of the weighting scalars φ, σ , ξ , ψ, η, and μ determine their relative weighting in the optimization cost function and depends on the desired performance objectives that the designer seeks to achieve (Lee et al, 2015). Now, based on the definition of the desired controlled vector z(t), the LPV output-feedback controller is designed for the SPMSM to minimize the induced L 2 gain (or H ∞ norm) (15) of the closed-loop LPV system (14).…”
Section: Robust Lpv Control Design For Spmsmmentioning
confidence: 99%
“…The velocity tracking error which is included in the second state x 2 (t) = e w (t) is penalized by the weighting scalar σ and the control efforts u α (t), u β (t) are penalized by the weighting scalars η and µ, respectively. The choice of the weighting scalars φ, σ, ξ, ψ, η, and µ determine the relative weighting in the optimization scheme and depends on the desired performance objectives that the designer seeks to achieve Lee et al (2015). Now, based on the definition of the desired controlled vector z(t), the output-feedback controller is designed for the SPMSM to minimize the induced L 2 gain (or H ∞ norm) (15) of the closed-loop LPV system (14).…”
Section: Spmsm Lpv Control Designmentioning
confidence: 99%