Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Abstract

-This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.

A Nonlinear Sliding Mode Controller for IPMSM Drives with an Adaptive Gain Tuning Rule

“…Despite their inherent advantages, the accurate speed control of IPMSM drives presents some difficult challenges in the presence of nonlinear coupling terms [4]. In addition, system uncertainties such as external disturbances and motor parameter variations can considerably deteriorate the control performances [5]. Accordingly, it is not easy for conventional PI controllers or LQ regulators to achieve good performance for IPMSM drives under the system uncertainties stated above [6].…”

“…Substituting (3) into (4) yields the following speed dynamic equation: where the k 1 to k 11 are the coefficients defined in [5]. In addition, taking into consideration the system uncertainties such as motor parameter variations, external disturbances, etc., the system model (6) can be rewritten as follows: …”

Adaptive PID Speed Control Design for Permanent Magnet Synchronous Motor Drives

“…Despite that, the PMSMs are gradually taking over the IMs owing to their high efficiency, low maintenance cost, and high power density. However, the PMSM system is not easy to control because it is a nonlinear multivariable system and its performance can be highly affected by parameters variations in the run time [6]- [9]. Therefore, researchers always desire to design a high-performance controller which has a simple algorithm, fast response, high accuracy, and robustness against the motor parameter and load torque variations.…”

PMSM Servo Drive for V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Chebyshev NN Control System

“…The models obtained with this approach are in state-space and work quite effectively in continuoustime domain. Presently, most of the NN-based system identification techniques are based on multilayer feedforward NNs or more efficient variation of this algorithm [12][13][14][15]. This is due to the fact that these networks are robust and effective in modeling and control of complex dynamic plants [12][13][14][15].…”

“…Artificial neural networks (ANNs) have emerged as a powerful learning technique to perform complex tasks in highly nonlinear dynamic environments [8][9][10][11][12][13][14][15]. Some of the prime advantages of using NN are: their ability to learn based on optimization of an appropriate error function and their excellent performance for approximation of nonlinear functions.…”