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Magnetically shielded rooms (MSRs) provide a near-zero field environment for magnetoencephalography (MEG) research. Due to the high cost of high-permeability materials and the weak shielding capability against low-frequency magnetic disturbance, it is necessary to further design active compensation coils combined with a closed-loop control system to achieve dynamic nulling of environmental magnetic disturbance. To enhance the performance of the dynamic nulling system, this paper proposes a novel controller design method based on Padé approximation and generalized active disturbance rejection control (GADRC). First, a precise closed-loop model of the dynamic nulling system is established. On this basis, the delay element of the optically pumped magnetometer (OPM) is approximated using the Padé approximation method, and the controller is designed within the GADRC framework. The system's stability and disturbance suppression capability are analyzed using frequency-domain methods. To validate the effectiveness of the proposed method, simulations and experiments are conducted, achieving a shielding factor greater than 40 dB at 0.1 Hz. After filtering out power frequency interference, the peak-to-peak field fluctuation is reduced from 320.3 pT to 1.8 pT. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]