Fractional order PI speed control for permanent magnet synchronous motor drives

Abstract

In this paper, a fractional order proportional and integral (FOPI or PI α ) speed control method for permanent magnet synchronous motor (PMSM) is presented. Based on the modeling of current loop in PMSM vector control, the speed PI α controller is designed which fulfills specifications of the given phase margin and open loop gain crossover frequency. Numerical solution of phase crossover frequency is executed for each integral order by using Newton iteration method. The corresponding gain margin is achieved afterwards. Furthermore, the parameters of PI α controller are tuned by selecting the maximum gain margin of speed loop. The simulation results show that the speed step has small overshoot and fast response, which can verify the effectiveness of the proposed control strategy.Index Terms -Permanent magnet synchronous motor (PMSM), vector control, fractional order controller (FOC), FOPI (PI α ) controller.

Speed Tracking for IFOC Induction Motor Speed Control Using Hybrid Sensorless Speed Estimator Based on Flux Error for Electric Vehicles Application

“…where T and T −1 are Park and inverse Park transformations, respectively. The stator flux in (13) acts as the feedback to the stator flux controller, which gives the compensated back-EMF error at a reduced level.…”

“…These parameters vary during operation due to temperature changes and the magnetic saturation effect, which deteriorate the IM performance and make it operate at low efficiency [12]. The issue was resolved in [13][14][15], where a tuning method (PI tuner) was used to tune the controller's response. The tuning process focused on the variable speed operation without considering the load disturbance rejection.…”

Fractional-Order PI Control of DFIG-Based Tidal Stream Turbine

“…Some researchers have already focused on the application of FOPI controller in the machine system [39,41,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]. These references mainly focused on the controller parameters tuning methods according to different criteria.…”

“…In [41,46], MATLAB optimization toolbox FMINCON was adopted to minimize the phase margin; Newton iteration method was selected to maximize the gain margin in [47]; ITAE was used as the criterion of optimal parameters tuning in [48][49][50][51]; reference [48] applied PSO to find the minimum ITAE; Dieng A. utilized PSO to optimize a special objective function which could minimize the torque ripple [52][53][54]; in [55], a new non-linear function containing ITSE, settling-time, rising time, and maximum overshoot was minimized by PSO and BFO. There are still a few papers studying on the robustness of internal parameter variations and external disturbances [39,56,57].…”

Fast and Robust Hybrid Starter and Generator Speed Control for Improving Drivability of Parallel Hybrid Electric Vehicles

“…It is not easy to fully satisfy all of the above conditions with conventional linear control methods, such as proportional-integral (PI) control [3,4], integral-proportional (IP) control [4], and two-degree-of-freedom (2-DOF) control [5]. Moreover, adaptive control [6], robust control [7], slide mode control [8,9], disturbance observer based control [10], finite-time control [11], fractional order control [12], fuzzy control [13,14], neural network control [15,16], and many other control methods have been proposed to improve the speed control performance, each of which improves it in various ways. However, these control methods have complicated control structures, require large computational resources, and are not easy to design for a desired control bandwidth.…”