Guo Qing-ding

The Diagnosis Method for Converter Fault of the Variable Speed Wind Turbine Based on the Neural Networks

Because the converter circuit model of the doubly-fed wind turbine has strong nonlinear, it is difficult to diagnose fault online usually. In this paper, using the nonlinear mapping characteristic of the neural networks, a BP neural networks fault diagnosis method which bases on wave direct analysis is proposed, which achieves the wind turbine converter circuit fault diagnosis online. The actual run results show the method is effective.

Sensorless control of permanent magnet synchronous motor

This paper introduces a sensorless control method of position estimation for permanent magnet (PM) synchronous machines. The method, using rotating vector, carrier frequency excitation and heterodyning, tracking observers for position estimation is discussed. The experimental results for the case of a buried PM machine are shown in the paper. It has been demonstrated that this technique can operate over a wide speed range.

Robust fuzzy variable structure control of PMLSM servo system

The paper proposes a new position control strategy which is robust against load disturbance, torque ripple and parameter variations. The strategy is an expansion of disturbance observer based on acceleration control loop. According to the current and position information, the disturbance observer estimates the disturbance and parameter variations and constructs the feedforward force loop. As a robust control system based on disturbance observer sometimes has high overshoot and oscillated response, both the feedback controller and disturbance observer adopt fuzzy variable structure construction so that observer noise is suppressed. The effectiveness of the proposed controller is verified by experimental result. Disturbance suppression and improvement of phase delay are attained.

Neural network real-time IP position controller on-line design for permanent magnetic linear synchronous motor

This paper presents a real-time IP position controller realized by a neural network for a permanent magnetic linear synchronous motor (PMLSM) servo system. The proposed neural network, whose weight definitely has material meaning, is simple and can be rapidly adjusted on-line, and the real-time position control for PMLSM is accomplished. In order to improve the robustness and the control precision of a PMLSM drive system, the mover mass, viscous damping factor and disturbance force are estimated by the proposed estimator which is composed of a recursive least-squares estimator (RLSE) and a disturbance force observer A simulation demonstrates that the proposed IP controller makes the system more robust to the uncertain load and the variation of the parameters.

Sensorless control of permanent magnet linear synchronous motor based on nonlinear observer

This paper presents a sensorless control method, which can estimate the stator magnetic pole position and mover speed by detecting the voltage and current of permanent magnet linear synchronous motor based on nonlinear observer. The H/sub /spl infin// control theory is used to guarantee good performance at low speed. A two-degree-of-freedom controller is obtained by solving model-matching problem. Simulation results show that the system can satisfy the servo requirement of tracking and disturbance suppressing in a wide speed range.

A shaft sensorless control for PMSM using direct neural network adaptive observer

Rotor position detection is necessary for phase commutation and current control in high-performance PMSM. The traditional detecting method is based on resolver, absolute encoder etc. This paper presents a position and velocity sensorless control algorithm based on a direct neural model reference adaptive observer. The proposed observer comprise two neural networks which are trained to learn the electrical and mechanical model respectively. Adaptation is realized by online training using current prediction error. Various advantages of this estimating scheme over other sensorless control schemes, such as robustness, nonlinear adaptation and learning ability is shown by extensive simulations.

A Novel DSP Based Current Controller with Fuzzy Variable-Band hysteresis for Active Power Filters

Conventional hysteresis current controller with fixed-band for active power filter (APF) has a high maximum switching frequency (MSF). To limit the MSF within the limit of inverter switches, a novel fuzzy hysteresis band current controller for active power filter is proposed in this paper. The band height, based on fuzzy control principle, is changed with the value of the error current and the rate of change of the error current, consequently the switching frequency is controllable. The fuzzy hysteresis band current control and harmonics compensation algorithm are both based on digital signal processor (DSP), which reduce the analogue circuitry and enhances the systems immunity to noise. The simulation results by MATLAB show that control strategy proposed is feasible

Completely digital PMSM servo system based on new self-tuning PID algorithm and DSP

This paper describes a high performance fully digital implementation of a permanent magnet synchronous motor (PMSM) servo system. On the basis of the vector control principle, the system is controlled by an INTEL 8031 microprocessor and a TMS320C25 DSP chip. A new self-tuning PID algorithm based on LATE optimal rule is used in the speed loop. The experimental result demonstrates that the system is capable to improve the performance in terms of precision of control action, dynamic response and flexibility, even when the system parameters are varied.

Synchronous Control of Gantry Moving Type Boring-Milling Machining Centers Based-on Disturbance Observer

For gantry-moving type boring-milling machining centers, although the two sides pillars of the gantry have the same drive mechanisms, the synchronous error between two drifts of the dual linear motor is still generated by non-balanced forces adding to the cross rail, blade carrier and other moving parts, position variation of blade carrier and various uncertainty disturbances during the working process. The synchronous error causes mechanical coupling and makes the synchronous movement of the two pillars difficult. For this, the disturbance observer is designed in dual linear motor X-drifts of gantry-moving type boring-milling machining to limit the asynchronization caused by the parameters variation and external torques in this paper. Besides, load dynamic compensation of Y-drift is applied to decrease disturbance to X-drifts. The simulation results indicate that the proposed scheme has stronger robustness and better rapidity, and smaller dynamical synchronous error. So the control method can meet the demand of the controlled plant in the high precision synchronous control

Preview feedforward compensation of permanent magnet linear synchronous motor servo system implemented with Adaline

After presenting briefly IP position control for a high-precision microfeed linear permanent magnet synchronous motor (LPMSM) servo system in this paper, we present a method of optimal preview feedforward compensation for this system to improve the tracking performance. To adapt the parameter variability of this system, we adopt Adaline to implement preview feedforward compensation. The input vector of Adaline is the preview steps,and its weight value is the preview feedforward coefficient. It is obvious that the weight has explicit physical meaning,and that the initial value of weight is not random but is the preview feedforward coefficient worked out under the rated condition, so the convergence speed of Adaline is very fast. To demonstrate the significance of this proposed method, simulation results of this application are carried out.