In this paper, a new sliding mode control strategy is proposed to be applied to the direct instantaneous torque control (DITC) of SRM. Based on the terminal sliding mode control, the load torque estimation value is added to the control law to improve the anti-interference ability of the system, and the controller parameters are adjusted online through the fuzzy control strategy to sup-press the torque ripple caused by SRM in the dynamic process, Finally, the convergence characteristics of the system are analyzed by Lyapunov stability theory. In order to verify the effectiveness of the system, simulation experiments are carried out in MATLAB/Simulink. The results show that the controller mentioned in this paper has good dynamic characteristics. While ensuring the response speed, it can well suppress the torque ripple when the motor starts and the given parameters change, sup-press the rising trend of current, and optimize the operation state of SRM.
Distributed networks in an insecure environment may have malicious attack nodes, which will tamper with their observation and communication data and affect the accuracy of parameter estimation. This paper proposes an improved Diffusion Least Mean Square (DLMS) algorithm with an adaptive punishment mechanism under malicious attacks. The algorithm uses median filtering to process the received neighbor node estimates, effectively filtering malicious nodes that have a greater impact. The location of malicious nodes is detected by designing an adaptive threshold. The combination weight of malicious nodes will be correspondingly reduced due to the existence of the punishment factors, thereby effectively weakening the impact of malicious nodes on network attacks. The simulation results show that the algorithm is robust under malicious attacks, can effectively resist data tampering attacks, and improve the overall estimation performance of the system.
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