High-Accuracy and Fast-Response Flywheel Torque Control

Compared with current mode flywheel torque controller, speed mode torque controller has superior disturbance rejection capability. However, the speed loop delay reduces system dynamic response speed. To solve this problem, a two-degrees-of-freedom controller (2DOFC) which consists of a feedback controller (FBC) and a command feedforward controller (FFC) is proposed. The transfer function of FFC is found based on the inverse model of motor drive system, whose parameters are identified by recursive least squares (RLS) algorithm in real-time. Upon this, Kalman filter with softening factor is introduced for the improved parameters identification and torque control performances. Finally, the validity and the superiority of the proposed control scheme are verified through experiments with magnetically suspended flywheel (MSFW) motor

New fault tolerance method for open-phase PMSM

Once the motor stator winding is opened, balanced three-phase windings turn into unbalanced two-phases windings. Unfortunately, by conducting Clarke and Park transformation for open-phase PMSM, complete decoupling of the torque and flux cannot achieve. To maintain the rated torque, the two remained phase currents have to be modified as sinusoidal currents with 60◦ phase difference (not 120◦). As a result, the current controller design becomes complicated. In order to solve this problem, a new fault tolerance method for the open-phase PMSM is proposed in this paper. It is designed based on a novel reference frame transformation. Through proposed frame transformation, the modified sinusoidal time-varying current commands are turned into dc variables in the redefined synchronous rotating frame. Hence, the design of the open-phase PMSM current controller can be simplified. This method can deal with different phase open fault and different current control mode (id = 0 or id 6= 0 mode). In addition, considering that the neutral current ripple at usual switching frequencies may be very high, an optimal additional inductance that inserted into the neutral wire is designed. With the designed additional inductance, complete decoupling can be achieved. Experimental results confirm that the reliability and the performance of the PMSM drive can be improved distinctly with the proposed open-phase fault tolerance strategy

Sensorless Energy Conservation Control for Permanent Magnet Synchronous Motors Based on a Novel Hybrid Observer Applied in Coal Conveyer Systems

A large number of permanent magnet synchronous motors (PMSMs) are used to drive coal conveyer belts in coal enterprises. Sensorless energy conservation control has important economic value for these enterprises. The key problem of sensorless energy conservation control for PMSMs is how to decompose the stator current through estimating the rotor position and speed accurately. Then a double closed loop control for stator current and speed is formed to make the stator current drive the motor as an entire torque current. In this paper, the proposed startup estimation algorithm can utilize the current model of PMSM as reference model to estimate the rotor speed and position in the startup stages. It is not dependent on the back electromotive force (EMF) which is used by the general estimation algorithm. However, the resistance will change with the temperature shift of stator windings, and these changes will cause the reference current model to be inaccurate and influence the rotor speed and position estimation precision. Thus, startup estimation algorithm switches to the proposed operation estimation algorithm which is based on the robust sliding mode theory and is not dependent on the motor parameters. The advantages of startup estimation algorithm and operation estimation algorithm are combined to form a hybrid observer. This hybrid observer realizes the accurate estimation of the rotor speed and position from start-up to operation. The stator current is precisely decomposed. The excitation current is controlled to 0. Meanwhile, the double closed-loop control of current and speed is achieved. The stator current is as entire torque current to drive motor. The closed-loop control, which is based on the proposed rotor position and speed estimation algorithm, achieve the most efficient conversion of electrical energy

Fault Diagnosis of Reciprocating Compressor Using Component Estimating Empirical Mode Decomposition and De-Dimension Template with Double-Loop Correction Algorithm

This paper presents an approach to implement multi-parameter (i.e., pressure, temperature, vibration, current, and liquid level) signals for fault diagnosis of the reciprocating compressor (RC). Due to the complexity of structure and motion of such compressor, the acquired signals involve transient impacts and noises. This causes the useful information to be corrupted and makes it difficult to diagnose the fault patterns accurately. A component estimating empirical mode decomposition (CEEMD) method is proposed to remove the random noise and improve data quality. Furthermore, a new template matching algorithm called de-dimension template with double-loop correction (DDT-DLC) is applied to diagnose the fault pattern contained in the time series signals. The DDT employs a judging criterion for key characterization parameters extraction and a multicellular parameter fusion method to reduce the dimension of the matching template, and then, the DLC supplies a double-loop correction algorithm to build a parameter state array computing model of the time series data by adjusting the dynamic factors. The proposed approach is validated with three fault patterns and the healthy pattern in a two-stage reciprocating air compressor. To confirm the superiority of the proposed method, its performance is compared with that of the traditional methods. The results have indicated that the proposed approach is of highly diagnostic accuracy and shortly computing time in the fault diagnosis

Neural Network Sliding Model Control of Radial Translation for Magnetically Suspended Rotor (MSR) in Control Moment Gyro

For a magnetically suspended control moment gyro (MSCMG), the high-speed rotor is actively suspended by magnetic bearings of 5-DOF, but the nonlinearity of the magnetic suspension force is one of the main reasons for the poor accuracy of radial translation control of the magnetically suspended rotor (MSR). To solve this problem, here, the characteristics of the magnetic suspension force are analyzed, and the nonlinear dynamic model of MSR is established. A sliding mode control (SMC) based on a neural network is presented, and the radial basis function (RBF) neural network is adopted to approximate the nonlinear displacement stiffness and the current displacement stiffness to weaken the chattering in SMC to improve the control accuracy of the MSR. The stability of the neural network SMC for the MSR is analyzed based on Lyapunov functions, and the rules of updating network weights are presented based on adaptive algorithms. Compared with these existing classic control methods, the simulation and experimental tests performed on a single-gimbal MSCMG with an angular momentum of 200 N.m.s indicated that this neural network SMC for MSR’s radial translation can not only make its suspension more stable but can also make its position precision higher