Abstract
Since the permanent magnet synchronous motor system in this research needs about 40 ms to finish a control cycle, such a long delay in time strongly causes the bad performance for the conventional controllers, especially for position control. To well control the speed and position, an adaptive neural predictive control is proposed. A two-layer recursive neural network is employed as a speed predictor, and an extended Kalman filter is utilized to tune the parameters of the predictor adaptively. Chaos optimization algorithm and Newton-Raphson optimization are combined to solve the problem of predictive control. As for the speed control, the proposed method shows better performance. The position control is designed based on the speed control. Due to the physical limitation of the plant, the steady state error is still large. Hence, a fuzzy compensator is applied. From the experiment, the error is reduced obviously.
Highlights
Sampling period is set to about 0.1ms to control the motors
Lin: Adaptive Neural Predictive Control for Permanent Magnet Synchronous Motor Systems With Long Delay Time performance of d −q axis stator currents to the command current, a robust feedforward control is combined with a simple adaptive observer which is used to estimate the disturbance in [11] while a robust controller based on time delay control approach is proposed in [12]
The Tent-map Chaotic Newton-Raphson (TCNR) algorithm combining the reliability of NR and the ergodicity property of chaos optimization algorithm (COA) is proposed in this study
Summary
Sampling period is set to about 0.1ms to control the motors. the permanent magnet synchronous motor (PMSM) servo system which is composed of a PMSM motor, and the variable frequency drive (VFD) in this study needs about 40ms to finish a control cycle. Lin: Adaptive Neural Predictive Control for Permanent Magnet Synchronous Motor Systems With Long Delay Time performance of d −q axis stator currents to the command current, a robust feedforward control is combined with a simple adaptive observer which is used to estimate the disturbance in [11] while a robust controller based on time delay control approach is proposed in [12]. The original idea of MPC only takes the future outputs generated form the model of the plant into considerations, and the past response is ignored, so there is no guarantee of the steady state error in cases of disturbances and inexact system modeling To fix this problem, [18], an integrator based compensator is added in the MPC structure. The performance of classical controller is not good and even terrible To deal with this problem, an adaptive neural predictive control is proposed to gain a better speed and position responses.
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