This article proposes a data-driven online diagnosis method for detecting common electrical faults and current sensor faults (CSFs) in dual three-phase permanent-magnet synchronous motor (DTP-PMSM) drives. The proposed diagnosis method fulfills feature extraction by obtaining the average values of phase currents in the positive and negative half-cycles of fault features to reduce dimensionality. By designing a one-vote veto classification rule, the proposed diagnosis method can accurately distinguish 24 fault modes and effectively prevent misdiagnosis. The classification model consists of only 24 decision functions, making it easy to implement on the mainstream industrial microcontrollers. The experiments are carried out to verify the validity of the proposed method.

Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faulty position and severity of the inter-turn short-circuit (ITSC) fault, which is one of the most destructive and frequent faults in PMSM. Compared with the existing research results gained by particle swarm optimization algorithms, in this paper, the methods using other optimization algorithms incorporating genetic algorithm, whale optimization algorithm and stochastic parallel gradient descent algorithm (SPGD) can acquire more stable and precise results. In particular, the method based on SPGD can obtain the most desirable performance among the methods mentioned above; that is, the relative error of short-circuit turns ratio is approximately as low as 0.03%. In addition, in the case of asymmetric input three-phase voltage and with the adverse impact of high-order harmonics at different load moments, the fault diagnosis method based on SPGD still maintains relatively satisfactory properties. Finally, the verification on the actual PMSM platform demonstrates that the SPGD can still diagnose the faulty severity.

The electro-optical tracking system (ETS) on moving platforms is affected by the vibration of the moving carrier, the wind resistance torque in motion, the uncertainty of mechanisms and the nonlinear friction between frames and other disturbances, which may lead to the instability of the electro-optical tracking platform. Sliding mode control (SMC) has strong robustness to system disturbances and unknown dynamic external signals, which can enhance the disturbance suppression ability of ETSs. However, the strong robustness of SMC requires greater switching gain, which causes serious chattering. At the same time, the tracking accuracy of SMC has room for further improvement. Therefore, in order to solve the chattering problem of SMC and improve the tracking accuracy of SMC, an SMC controller based on internal model control (IMC) is proposed. Compared with traditional SMC, the proposed method can be used to suppress the strongest disturbance with the smallest switching gain, effectively solving the chattering problem of the SMC, while improving the tracking accuracy of the system. In addition, to reduce the adverse influence of sensor noise on the control effect, lifting wavelet threshold de-noising is introduced into the control structure to further improve the tracking accuracy of the system. The simulation and experimental results verify the superiority of the proposed control method.

In this article, an online current optimization-based fault-tolerant (FT) control is proposed to improve the FT capability of standard permanent magnet synchronous machine (PMSM) drive with open-phase fault (OPF) and open-switch fault (OSF). In the proposed FT control, the torque ripple, overcurrent endurance, and copper loss are considered to optimize the FT current reference for OPF and OSF. The optimized current reference can be obtained online by calculating the average copper loss in the OPF and OSF FT operation. By using the optimized current reference, the current tracking error can be eliminated, and a good balance between torque ripple and copper loss can be fulfilled. In this way, the torque output capability can be improved after the average copper loss is restricted. The effectiveness of the proposed FT control has been verified by experiments.

The dynamic Moore-Penrose inverse solution has attracted increasing attention because of its wide range of applications. The use of zeroing neural networks to solve the inverse problem of dynamic matrices has become a popular topic in recent years. However, existing studies have established ZNN models for the left Moore-Penrose inverse and right Moore-Penrose inverse based on the different dimensions of the matrix, respectively. There is an urgent need for a new framework that can handle both the right inverse and the left inverse simultaneously. In addition, convergence performance is a critical metric when using neural networks to solve problems, regardless of whether the environment has measurement noise or not. Therefore, this paper proposes a unified solution framework that can be used to simultaneously solve for the left Moore-Penrose inverse and the right Moore-Penrose inverse. Two novel ZNN models that converge in finite time are obtained by using the evolution formula based on finite-time convergence. The convergence and robustness of the models are analyzed using finite-time theory. The performance of the proposed method is compared to that of existing methods using numerical examples. Furthermore, the proposed neural network models for calibration have been implemented in a photoelectric tracking system that utilizes a redundant manipulator. The feasibility and effectiveness of the proposed model in the actual system have been verified through simulations and experiments. It has been observed that the root mean square value of tracking error is as low as 0.032 mm in the experiment.