Ran Tao

Driven by the electrification of transportation, there is a growing demand for high-performance control strategies in electric traction systems, particularly for Permanent Magnet Synchronous Motors (PMSMs). However, variations in internal parameters and external disturbances can significantly degrade control performance. To address these challenges, this paper introduces a novel Model-Free Adaptive Second-Order Sliding Mode Control (NMF-ASOSMC) strategy. First, an ultra-local model of the system is derived from a detailed analysis of PMSM dynamics. A second-order sliding mode speed controller is then designed, replacing the traditional switching function with a terminal attractor and integrating an adaptive law to minimize chattering. Furthermore, a sliding mode observer is employed to estimate disturbances in the speed loop in real time and provide appropriate compensation. Experimental results demonstrate the effectiveness and feasibility of the proposed strategy in improving control performance.

To address the key challenges of parameter sensitivity and inverter nonlinear disturbances in dual three-phase permanent magnet synchronous motor (DT-PMSM) drive systems, an enhanced integral sliding mode observer (E-ISMO)-based model-free predictive current control (E-MFPCC) method is proposed. Firstly, the significant influence of parameter mismatch and inverter nonlinearity on the control performance is deeply analyzed. Based on this, an enhanced integral sliding mode observer is designed. Compared with the traditional method, the proposed method has higher disturbance estimation accuracy, which brings excellent control performance under parameter mismatch and inverter nonlinearity conditions while no additional inverter nonlinearity compensation algorithms are required. Meanwhile, the system stability is strictly proved based on Lyapunov theory, which ensures the theoretical reliability of the method. Finally, a DT-PMSM experimental platform is constructed and the experiment results verify the effectiveness of the method.

A full-torque copper-loss minimum adaptive fault-tolerant control (FTCM-AFTC) method is proposed in this article for a dual three-phase permanent magnet synchronous motor (DTP-PMSM) drive system under an open-phase fault. In the proposed FTCM-AFTC, a unified control architecture is constructed to realize seamless operation under healthy and fault conditions. The proposed FTCM-AFTC method can smoothly transition after fault occurrence without additional hardware. In addition, the method can dynamically adjust FTCM according to torque demand to ensure optimal operation efficiency. Finally, the effectiveness of the proposed FTCM-AFTC method is verified by the experimental results.

The guest room and aisle of electric high-speed maglev train must be shielded from leakage magnetic flux produced by superconducting strong magnetic field. To reduce magnetic leakage, the superconducting magnetic levitation system structure is obtained by extended lagrangian optimization method. The optimized superconducting coil structure has the advantages of reducing magnetic leakage, improving magnetic field utilization, reducing the weight of the magnetic isolation plate and the weight of the maglev train, and enhancing the load-bearing capacity of the maglev train. Based on optimized superconducting coil parameters for high-speed maglev, the magnetic shielding effect at the aisle and the guest room, the magnetic flux density distribution at the magnetic shielding is calculated and analyzed through analytical calculation. The relevant conclusions indicate that the magnetic suspension structure has the advantages of reducing end coil leakage flux and the weight of the high-speed maglev train.

Aiming at the control performance degradation of permanent magnet synchronous motor (PMSM) drive systems caused by uncertainties of internal and external disturbances, a robust control algorithm integrating an improved fast terminal sliding mode (IFTSM) surface with a novel adaptive reaching law (NARL) is proposed. A dynamic model of PMSM with disturbances is established, and an improved fast terminal sliding mode surface is designed. By introducing nonlinear terms and error derivative feedback mechanisms, the finite-time rapid convergence of system states is achieved, while solving the singularity problem of traditional terminal sliding mode control. Combined with the novel adaptive reaching law strategy, a state-dependent gain adjustment function is used to dynamically optimize the balance between reaching speed and chattering, enhancing the smoothness of the system′s dynamic response. Through the synergy of the finite-time convergence characteristic of the improved sliding mode surface and the novel adaptive reaching law, the proposed algorithm significantly enhances the system′s anti-interference capability against load mutations and parameter time variations. Experiment results demonstrate that under complex working conditions, the algorithm achieves superior speed tracking accuracy and current stability, providing a control solution with strong anti-interference capability and fast response for PMSM speed control systems.

This paper proposes a Free-Current-Curve fault-tolerant control (FCC-FTC) method for a Dual Three-Phase Permanent Magnet Synchronous Motor (DT-PMSM) drive system under an open-phase fault. In the proposed FCC-FTC, apart from the basic operational constraints (e.g., limiting the RMS phase currents, ensuring torque, and maintaining zero current sum), no sinusoidal phase current constraint is involved while the fault-tolerance performances can be optimized with maximum freedom. According to different torque output command, the proposed FCC-FTC method can be switched between the Loss Mode and the Torque Mode. In the Loss Mode, the system copper loss can be minimized. In the Torque Mode, the system torque capacity can be maximized. Compared with existing methods, the proposed FCC-FTC method has lower copper loss and stronger torque output capability. An experiment platform is set up on a DT-PMSM, and the effectiveness of the proposed FCC-FTC is validated by experimental results.

The conventional single variable indirect model predictive control (SIMPC) of LCL‐type three‐level grid‐ connected inverter (GCI) is to indirectly control the grid current by controlling the output current of the inverter, which usually produces large errors and reduces the system anti‐disturbance performance. Although the previously proposed multivariable weighted model predictive control (MWMPC) method of LCL‐type GCI can directly control many variables, such as grid current, it requires setting the weight coefficient for each variable and involves a substantial amount of calculation. To due with these problems, this paper proposes a multivariable sequential model predictive control (MSMPC) method for LCL‐type three‐level GCI. The inverter output current, the filter capacitor voltage, and the grid current are gradually predicted and optimized under the variable control sequence of the LCL‐type GCI mathematical model. Under the premise of achieving direct control of the variables above, this method can reduce the number of weighting coefficients and gradually contract the optimization space to reduce the computation required for multivariable prediction and optimization. This paper compares the proposed MSMPC to the conventional SIMPC and MWMPC in steady state, transient state performance, and anti‐disturbance performance. The effectiveness of the proposed MSMPC is demonstrated through simulation and experimentation.

Finite control set model predictive control (FCS‐MPC) is becoming an important control method in the high‐performance control of permanent magnet synchronous motor (PMSM) because of its simple concept, easy implementation, and strong performance. However, the traditional FCS‐MPC uses only one basic voltage vector in the whole control cycle, so its steady‐state control performance is poor. In addition, FCS‐MPC relies significantly on machine parameter correctness. The performance is significantly degraded if the controller parameters do not match the real machine parameters. This study proposed a PMSM fast two‐vector model‐free predictive control approach to address the abovementioned issues. The process employs no motor parameters and relies only on the system's inputs and outputs. In addition, this method combines the fast vector selection with the two‐vector control method, which simplifies the computational effort while improving the steady‐state control accuracy at the same time. MATLAB/Simulink built the control system model, the simulation and experimental studies were carried out. The simulation and experimental results confirm the effectiveness of this method.