Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and opt...Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and optimization process is investigated.The general idea of using ML is to train surrogate models for the optimization.This training process is based on sample data collected from detailed simulation or experiment of motor drives.However,the Surrogate Role(SR)of ML may vary for different applications.This paper first introduces the principles of ML and then proposes two SRs(direct mapping approach and correction approach)of the ML in a motor-drive optimization process.Two different cases are given for the method comparison and validation of ML SRs.The first case is using the sample data from experiments to train the ML surrogate models.For the second case,the joint-simulation data is utilized for a multi-objective motor-drive optimization problem.It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case,three feasible design schemes of ML are proposed and validated for the two SRs.Regarding the time consumption in optimizaiton,the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.展开更多
This paper presents an auto-tuning method for a proportion plus integral（PI） controller for permanent magnet synchronous motor（PMSM） drives, which is supposed to be embedded in electro-mechanical actuator（EMA） c...This paper presents an auto-tuning method for a proportion plus integral（PI） controller for permanent magnet synchronous motor（PMSM） drives, which is supposed to be embedded in electro-mechanical actuator（EMA） control module in aircraft. The method, based on a relay feedback with variable delay time, explores different critical points of the system frequency response.The Nyquist points of the plant can then be derived from the delay time and filter time constant.The coefficients of the PI controller can then be obtained by calculation while shifting the Nyquist point to a specific position to obtain the required phase margin. The major advantage of the autotuning method is that it can provide a series of tuning results for different system bandwidths and damping ratios, corresponding to the specification for delay time and phase margin. Simulation and experimental results for the PMSM controller verify the performance of both the current loop and the speed loop auto-tuning.展开更多
This study uses the Neural Network(NN)technique to optimize design of surfacemounted Permanent Magnet Synchronous Motors(PMSMs)for More-Electric Aircraft(MEA)applications.The key role of NN is to provide dedicated cor...This study uses the Neural Network(NN)technique to optimize design of surfacemounted Permanent Magnet Synchronous Motors(PMSMs)for More-Electric Aircraft(MEA)applications.The key role of NN is to provide dedicated correction factors for the analytical PMSM mass and loss estimation within the entire design space.Based on that,a globally optimal design can be quickly obtained.Matching the analytical estimation with Finite-Element Analysis(FEA)is the main research target of training the NN.Conventional analytical formulae serve as the basis of this study,but they are prone to loss accuracy(especially for a large design space)due to their assumptions and simplifications.With the help of the trained NNs,the analytical motor model can give an estimation as accurate as the FEA but with super less time during the optimization process.The Average Correction Factor(ACF)approach is regarded as the comparison method to demonstrate the excellent performance of the proposed NN model.Furthermore,a NN aided three-stage-sevenstep optimization methodology is proposed.Finally,a Pole-10-Slot-12 PMSM case study is given to demonstrate the feasibility and gain of the NN aided multi-objective optimization approach.In this case,the NN aided analytical model can generate one motor design in 0.04 s while it takes more than 1 min for the used FEA model.展开更多
基金funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and Innovation Programme No.807081。
文摘Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and optimization process is investigated.The general idea of using ML is to train surrogate models for the optimization.This training process is based on sample data collected from detailed simulation or experiment of motor drives.However,the Surrogate Role(SR)of ML may vary for different applications.This paper first introduces the principles of ML and then proposes two SRs(direct mapping approach and correction approach)of the ML in a motor-drive optimization process.Two different cases are given for the method comparison and validation of ML SRs.The first case is using the sample data from experiments to train the ML surrogate models.For the second case,the joint-simulation data is utilized for a multi-objective motor-drive optimization problem.It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case,three feasible design schemes of ML are proposed and validated for the two SRs.Regarding the time consumption in optimizaiton,the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.
基金part funding for this work from the Clean Sky JTI – Systems for Green Operations ITDsupported by the National Natural Science Foundation of China (No. 50807002)+1 种基金the Aeronautical Science Foundation of China (No. 2008ZC51045)the Beijing Nova Program (No. 2008B13)
文摘This paper presents an auto-tuning method for a proportion plus integral（PI） controller for permanent magnet synchronous motor（PMSM） drives, which is supposed to be embedded in electro-mechanical actuator（EMA） control module in aircraft. The method, based on a relay feedback with variable delay time, explores different critical points of the system frequency response.The Nyquist points of the plant can then be derived from the delay time and filter time constant.The coefficients of the PI controller can then be obtained by calculation while shifting the Nyquist point to a specific position to obtain the required phase margin. The major advantage of the autotuning method is that it can provide a series of tuning results for different system bandwidths and damping ratios, corresponding to the specification for delay time and phase margin. Simulation and experimental results for the PMSM controller verify the performance of both the current loop and the speed loop auto-tuning.
基金funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 Research and Innovation Programme(No 807081)。
文摘This study uses the Neural Network(NN)technique to optimize design of surfacemounted Permanent Magnet Synchronous Motors(PMSMs)for More-Electric Aircraft(MEA)applications.The key role of NN is to provide dedicated correction factors for the analytical PMSM mass and loss estimation within the entire design space.Based on that,a globally optimal design can be quickly obtained.Matching the analytical estimation with Finite-Element Analysis(FEA)is the main research target of training the NN.Conventional analytical formulae serve as the basis of this study,but they are prone to loss accuracy(especially for a large design space)due to their assumptions and simplifications.With the help of the trained NNs,the analytical motor model can give an estimation as accurate as the FEA but with super less time during the optimization process.The Average Correction Factor(ACF)approach is regarded as the comparison method to demonstrate the excellent performance of the proposed NN model.Furthermore,a NN aided three-stage-sevenstep optimization methodology is proposed.Finally,a Pole-10-Slot-12 PMSM case study is given to demonstrate the feasibility and gain of the NN aided multi-objective optimization approach.In this case,the NN aided analytical model can generate one motor design in 0.04 s while it takes more than 1 min for the used FEA model.
