Gain Scheduling Adaptive Proportional-integral Controller for a Field-oriented Control of Hybrid Stepper Motor Drives

In this article, we have set up a vector control law of induction machine where we tried different type of speed controllers. Our control strategy is of type Field Orientated Control (FOC). In this structure we designed a Fuzzy Gain-Scheduling Proportional–Integral (Pi) controller to obtain best result regarding the speed of induction machine. At the beginning we designed a Pi controller with fixed parameters. We came up to these parameters by identifying the transfer function of this controller to that of Broïda (second order transfer function). Then we designed a fuzzy logic (FL) controller. Based on simulation results, we highlight the performances of each controller. To improve the speed behaviour of the induction machine, we have designend a controller called “Fuzzy Gain-Scheduling Proportional–Integral controller” (FGS-PI controller) which inherited the pros of the aforementioned controllers. The simulation result of this controller will strengthen its performances.

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dSPACE Implementation of Improved Indirect Rotor Field-Oriented Control of Asynchronous Motor using Fuzzy Self-Adjustable Proportional Integral Controller for Electric Vehicle Applications

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l2974.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2841-2847

Keyword(s):

Electric Vehicle ◽

High Speed ◽

Asynchronous Motor ◽

Field Oriented Control ◽

Proportional Integral ◽

Speed Tracking ◽

External Conditions ◽

Integral Controller ◽

Proportional Integral Controller ◽

Rotor Field Oriented Control

This paper deals with the indirect rotor field-oriented control of asynchronous motor whose speed is controlled by a fuzzy self-adjustable proportional integral controller. This motor drive is used to propel an electric vehicle. The designing and the implementing of the fuzzy self-adjustable proportional integral controller are presented. This controller is proposed as a solution to compensate for the effect of the machine parameters variation and the external conditions. The characteristic of this controller is its capacity to adapt in real time its gains in order to reject the machine parameters disturbances. A series of measurements has been achieved to prove the performances of the improved drive using the proposed controller. Experimental results showed the high-speed tracking and the rejection disturbances capacity of the fuzzy self-adjustable proportional integral controller.

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Comparative Analysis of Advanced Controllers for Standalone WECs for DC Microgrid Applications

10.4018/978-1-6684-4012-4.ch002 ◽

2022 ◽

pp. 38-82

Author(s):

Bhavya Dharmesh Pandya ◽

Siddharth Joshi

Keyword(s):

Comparative Analysis ◽

Wind Energy ◽

Closed Loop ◽

Simulation Analysis ◽

Gain Scheduling ◽

Proportional Integral ◽

Perturb And Observe ◽

Integral Controller ◽

Proportional Integral Controller ◽

Power Point

The small-scale wind energy generation system is one of the solutions to empower the isolated loads and provides a promising solution to decrease the greenhouse effect. This chapter describes the simulation analysis for wind energy conversion system incorporated with maximum power point tracking feature. The MPPT algorithms like variable current perturb and observe algorithm and variable step perturb and observe algorithm are incorporated with WECS. The comparative analysis is done in the closed-loop model in continuous time-varying wind speed. The closed-loop simulation is performed using a conventional fixed gain controller. To address the limitations of the fixed gain controller, the analysis is done using the gain scheduling proportional integral controller and the good gain method to tune the proportional integral controller. The comparative analysis between the fixed gain controller, the gain scheduling proportional integral controller, and the good gain method to tune proportional integral controller for above-stated MPPT methods is shown.

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