The traditional fault-tolerant procedure usually comprises fault detection and fault tolerance. Fault detection is adopted to locate the specific fault, which increases the complexity and misdiagnosis risk of fault-tolerant systems. This article introduced an adaptive fault-tolerant control for open-phase faults and open-switch faults in dual three-phase permanent magnetic synchronous machine (PMSM) drives. The proposed fault-tolerant scheme is fulfilled by going through three operation states: healthy operation, transient xy open-loop faulty operation, and fault-tolerant operation. The angle of fault-tolerant reference currents on xy plane is obtained from the transient xy open-loop faulty operation. Unified fault-tolerant reference currents of open-circuit faults are built up by using the above reference angle. After the open-circuit faults occur, the unified fault-tolerant reference currents are directly applied to the drive system without fault localization. Experimental results demonstrate a very smooth switching capability of the developed adaptive fault-tolerant control from the faulty operation to the fault-tolerant operation without extra hardware.

In this article, a novel optimization-based maximum-torque fault-tolerant control scheme is proposed for the open-phase fault of dual three-phase permanent-magnet synchronous motor drive. First, the relationship between torque output capability and single-phase copper losses under open-phase fault is derived with the consideration of overheating problems. Since the derived expression has multiple optimization goals, a new objective function is designed to simplify the current optimization. Second, particle swarm optimization (PSO) is utilized to obtain the maximum-torque current references based on the designed objective function. Different from the conventional maximum-torque fault-tolerant schemes which limit the current patterns, the proposed fault-tolerant control scheme employs PSO to search for the global optimal result without adding any unnecessary restrictions, such as sinusoidal current patterns. In this way, the proposed fault-tolerant control scheme can achieve the theoretical highest torque output capability of 73.3% with lower total copper loss. Both the theoretical and experimental results are presented to prove the effectiveness and superiority of the proposed optimization-based maximum-torque fault-tolerant control scheme.

This paper presents a hybrid non-singleton fuzzy strong tracking Kalman filter (H-NFSTKF) for the high precision photoelectric tracking system (PTS) to improve state estimation performance in the complex case of measurement noise change and velocity state mutation. The proposed H-NFSTKF is composed of three parts: a strong tracking Kalman filter (STKF) and two fuzzy logic systems (FLSs) including a singleton FLS (SFLS) and a non-singleton FLS (NFLS). To compare different non-singleton firing strength approaches, standard method (Sta-NS), centroid-based method (Cen-NS), similarity-based method (Sim-NS), and subsethood-based method (Sub-NS) are discussed. In addition, two traditional control strategies, i.e, dual closed-loop and feed-forward control, as well as four filtering methods including Kalman filter (KF), STKF, fuzzy STKF (FSTKF), and single non-singleton FSTKF (S-NFSTKF) are also designed to show the superiority of the proposed H-NFSTKF. Finally, by means of comparative simulation analyses and experimental results, the excellence of the proposed H-NFSTKF is verified.

To fulfill accurate online temperature estimation of permanent-magnet synchronous motor (PMSM), an integrated model-based and data-driven method is proposed in this article. First, a simplified lumped parameter thermal network (LPTN) model is developed to learn the tendency of temperature variations. Meanwhile, a small-scale artificial neural network (ANN) is specifically designed to compensate the unmodeled characteristics. The parameters of LPTN model in the proposed method is identified purely from the common variables and no material information is required. With the knowledge learned by the LPTN model and powerful fitting capability of ANN, accurate estimation for both stator and rotor temperatures can be achieved with low computational burden and reduced parameter dependency. Both offline and online experimental results are presented to prove the excellent performances of the proposed method.

In high-speed free-space optical communication systems, the received laser beam must be coupled into a single-mode fiber at the input of the receiver module. However, propagation through atmospheric turbulence degrades the spatial coherence of a laser beam and poses challenges for fiber coupling. In this paper, we propose a novel method, called as adaptive stochastic parallel gradient descent (ASPGD), to achieve efficient fiber coupling. To be specific, we formulate the fiber coupling problem as a model-free optimization problem and solve it using ASPGD in parallel. To avoid converging to the extremum points and accelerate its convergence speed, we integrate the momentum and the adaptive gain coefficient estimation to the original stochastic parallel gradient descent (SPGD) method. Simulation and experimental results demonstrate that the proposed method reduces 50% of iterations, while keeping the stability by comparing it with the original SPGD method.