Advanced Speed Control of PMSM Motor Using Neural FOC Method

Advanced Speed Control of PMSM Motor Using Neural FOC Method

ABSTRACT:

This paper investigates an artificial neural network (ANN)-based field-oriented control (NNFOC) for a surfacemounted permanent magnet synchronous machine (SPMSM) drive. The ANN was trained by using the modified Levenberg- Marquardt MLM algorithm. The novel Maximum Torque per Ampere (MTPA) and flux weakening (FW) approach are undertaken for electric vehicle (EV) application. The proposed control is validated in simulation tests and compared with a PIbased field-oriented control (PI-FOC). The simulation results show that the maximum operating speed of the proposed control is improved by 48% compared to the PI-FOC based control. Consequently the proposed neural NN-FOC controller can be a potential replacement of the existing control schemes, such as PID, fuzzy logic, or others, and provides adequate traction control in advanced electric drives, particularly in electric vehicle EV application.

KEYWORDS: surface-mounted permanent magnet synchronous machine (SPMSM), Neural-based fieldoriented control (NNFOC), electric vehicle EV application, Maximum Torque per Ampere (MTPA) control, flux weakening (FW) control, modified Levenberg-Marquardt MLM algorithm

SOFTWARE: MATLAB/SIMULINK CONVENTIONAL DIAGRAM:

Figure 1.PMSM conventional PI- and neural network NN-based FOC control

EXPECTED SIMULATION RESULTS:

Figure 2. Proposed NN-FOC control related to d- and q-axis current traction of SPMSM (simulation)

Figure 3. Conventional PI-FOC control related to d- and q-axis current traction of SPMSM (simulation)

Figure 4. Proposed NN-FOC control of abc current traction of SPMSM (simulation)

Figure 5. Conventional PI-FOC control related to abc current traction of SPMSM (simulation)

Figure 6. Proposed NN-FOC control related to normalized amplitude of voltage of SPMSM (simulation)

CONCLUSION: This paper proposes a novel neural-based field-oriented control (NN-FOC) for a surfacemounted permanent magnet synchronous machine (SPMSM) drive. The neural model is trained by modified Levenberg-Marquardt MLM algorithm. Furthermore the novel Maximum Torque per Ampere (MTPA) and flux weakening (FW) approaches are undertaken for PMSM-based electric vehicle (EV) application. The proposed control is validated in simulation tests and compared with a PI-based field-oriented control (PI-FOC). The simulation results show that the proposed neural NN-FOC controller demonstrates quite better performance than the conventional PI-FOC control and then provides an excellent traction method applied in advanced electric drives.

REFERENCES: [1] T. Lee, C. Lin, and F. Lin, “An Adaptive H∞ Controller Design for Permanent Magnet Synchronous Motor Drives,” Control Engineering Practice 13, pp. 425-439, 2005.

[2] S. Li, T.A. Haskew, and L. Xu, “Conventional and Novel Control [3] Designs for Direct Driven PMSG Wind Turbines,” Electric Power [4] System Research, vol. 80, pp. 328-338, March 2010.

[5] A. Mishra, V. Mahajan, P. Agarwal, and S.P. Srivastava, “Fuzzy Logic Based Speed and Current Control of Vector Controlled PMSM Drive,” 2012 2nd International Conf. on Power, Ctrl, and Embedded Sys, 2012.