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In this paper, a sliding mode vector control system based on collaborative optimization of an axial flux permanent magnet synchronous motor (AFPMSM) for an electric vehicle is proposed. In order to increase the high efficiency range of electric vehicles and improve the cruising range, a collaborative optimization control strategy is firstly proposed. Due to the use of a dual stator-single rotor AFPMSM, the multi-motor efficiency optimization map and torque cooperative control are used to realize the working mode conversion of single stator and double stator, and the torque ripple caused by the working mode conversion is improved by fuzzy control. In order to improve the torque tracking capability, speed limiting characteristics, and operating characteristics, a speed limit and current vector control strategy based on a sliding mode controller is proposed and studied. The dynamic performance of electric vehicles is improved by a sliding mode vector control. Finally, a drive control system was developed for the proposed control strategy, and the complete vehicle test was carried out. The collaborative optimization control experiment and torque tracking and speed limiting experiments verify the correctness and effectiveness of the proposed control strategy. The acceleration performance and endurance experiments show that the proposed control strategy can effectively improve the cruising range and the acceleration performance of electric vehicles.

In order to improve the cruising range of electric bus, this paper studies the deadbeat current prediction vector control system of axial flux permanent magnet synchronous motor (AFPMSM) for electric bus based on the optimal torque distribution method. Firstly, the mathematical model of the three statorsdouble rotors AFPMSM is established. Secondly, in order to improve the high efficiency range, the efficiency optimal torque distribution method is proposed based on the average torque distribution method and the back propagation (BP) neural network is used to find the optimal torque distribution method. Then a current control strategy based on deadbeat current prediction control is proposed to improve the torque tracking characteristics. Finally, a drive control system is developed for the proposed control strategy, and experimental research and vehicle testing are carried out. The experimental results show that the BP neural network-based torque distribution method designed in this paper increases the high efficiency range of the drive system and improves the cruising range of the electric bus. The drive system using a current controller based on deadbeat current prediction control exhibits good dynamic and steady state performance. INDEX TERMS Electric bus, AFPMSM, BP neural network, efficiency optimal torque distribution, deadbeat current prediction control.

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To improve the safety and economy of aircraft pallet use, an aircraft pallet damage monitoring method based on damage subarea identification and probability-based diagnostic imaging is proposed. In the proposed method, first, the large aircraft pallet monitoring area is divided into rectangular subareas, and a piezoelectric transducer sensor is pasted on each vertex of the rectangular subarea that is used to excitation and sensing the Lamb wave. Second, the damage subarea is identified according to the diagonal damage indexes. Third, the damage position in the damage subarea is calculated using the probability-based diagnostic imaging method and coordinate probability weighted algorithm. Finally, the aircraft pallet damage can be localized based on the damage subarea position. Frequency selection and damage simulation study results show that the Lamb wave is sensitive to aircraft pallet damage whose centre frequency ranges from 50 kHz to 150 kHz, and the damage index of a steel ball is less than that of all real aircraft pallet damage from 95 kHz to 125 kHz. The verification results show that the proposed method can locate aircraft pallet damage with an error of less than 2 cm.

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This paper proposes a fault diagnosis and fault-tolerant control method for a system with a fast time-varying delay and time-varying parameters. A fault observer is designed to estimate faults, and an improved fast adaptive fault estimation (FAFE) algorithm is developed to reduce the relevant constraints in the general form of this algorithm. With newly introduced relaxation matrices, this study estimates faults in a system exhibiting a fast time-varying delay. Based on the estimated faults, an output feedback controller is designed to accommodate the faults. The fault-tolerant control is realized using the introduced relaxation matrices. An algorithm is derived to solve for the observer and controller. Finally, the theory and method are validated using a real example of a helicopter system.