The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and fault-tolerant control in PMBLDC motor drives. Initially, the Hall sensor faults are analyzed and classified into three fault types. Taking the Hall signal as the system state and the conducted MOSFETs as the system event, the extended finite state machine (EFSM) of the motor in operation is established. Meanwhile, a motor speed observer based on the super twisting algorithm (STA) is designed to obtain the speed signal of the proposed strategy. On this basis, a real-time Hall sensor fault diagnosis strategy is established by combining the EFSM and the STA speed observer. Moreover, this article proposes a Hall signal reconstruction strategy, which can generate compensated Hall signal to realize fault-tolerant control under single or double Hall sensor faults. Finally, theoretical analysis and experimental results validate the superior effectiveness of the proposed real-time fault diagnosis and fault-tolerant control strategy.

This paper proposes a backstepping fuzzy sliding mode control method for the antiskid braking system (ABS) of unmanned aerial vehicles (UAVs). First, the longitudinal dynamic model of the UAV braking system is established and combined with the model of the electromechanical actuator (EMA), based on reasonable simplification. Subsequently, to overcome the higher-order nonlinearity of the braking system and ensure the lateral stability of the UAV during the braking process, an ABS controller is designed using the barrier Lyapunov function to ensure that the slip ratio can track the reference value without exceeding the preset range. Then, a power fast terminal sliding mode control algorithm is adopted to realize high-performance braking pressure control, which is required in the ABS controller, and a fuzzy corrector is established to improve the dynamic adaptation of the EMA controller in different braking pressure ranges. The experimental results show that the proposed braking pressure control strategy can improve the servo performance of the EMA, and the hardware in loop (HIL) experimental results indicate that the proposed slip ratio control strategy demonstrates a satisfactory performance in terms of stability under various runway conditions.

When the linear active disturbance rejection control (LADRC) is applied for the voltage-controlled inverter, the discrete period and the measurement noise limits the observer bandwidth, which affects the anti-disturbance performance of the system. This results in a poor ability to deal with the output voltage fluctuation under the load switch. In this paper, a novel LADRC strategy based on the known disturbance compensation is proposed for the voltage-controlled inverters. Firstly, the original LADRC scheme is designed. The dynamic performance and robustness of the system are analyzed by a root locus diagram, and the anti-disturbance ability is studied through amplitude-frequency characteristics. Then the partial model information and the load current are treated as the known disturbance and introduced to the linear extended state observer (LESO) to improve observation accuracy. The difference in anti-disturbance performance with the original scheme is compared and the stability of the LESO and LADRC is analyzed. Finally, the effectiveness of the proposed scheme is verified by the simulation and experimental results.

This paper proposes an adaptive backstepping control algorithm for electric braking systems with electromechanical actuators (EMAs). First, the ideal mathematical model of the EMA is established, and the nonlinear factors are analyzed, such as the deformation of the reduction gear. Subsequently, the actual mathematical model of the EMA is rebuilt by combining the ideal model and the nonlinear factors. To realize high performance braking pressure control, the backstepping control method is adopted to address the mismatched uncertainties in the electric braking system, and a radial basis function (RBF) neural network is established to estimate the nonlinear functions in the control system. The experimental results indicate that the proposed braking pressure control strategy can improve the servo performance of the electric braking system. In addition, the hardware-in-loop (HIL) experimental results show that the proposed EMA controller can satisfy the requirements of the aircraft antilock braking systems.

This paper proposes the design and application of a new nonlinear sliding mode controller for servo system of antenna with a brushless driving motor. First, the dynamic properties of the servo system are analyzed, and the system model is simplified and established. After that, a chattering free sliding mode control strategy is designed to deal with the nonlinearity caused by the permanent magnet brushless driving motor. Finally, the results and analysis of the controlled experiment show that the proposed nonlinear sliding mode control strategy can be applied in the actual antenna system and significantly improve the tracking accuracy and speed of the servo system.