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Acta Armamentarii ›› 2025, Vol. 46 ›› Issue (3): 240421-.doi: 10.12382/bgxb.2024.0421

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Nonlinear Sliding Mode Control Based on Neural Network Compensation for Tank All-electric Bidirectional Stabilizers

WANG Yimin1, YUAN Shusen2,*(), LIN Darui1, YANG Guolai1,**()   

  1. 1 School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
    2 National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  • Received:2024-05-29 Online:2025-03-26
  • Contact: YUAN Shusen, YANG Guolai

Abstract:

The control strategy of traditional tank bidirectional stabilizers is difficult to effectively deal with the coupling,nonlinearity and uncertainty in the new generation of all-electric bidirectional stabilizers,while the model-based nonlinear control can make full use of a priori information from the dynamic model of the system to enhance the control effect.Based on this,an electromechanical coupled dynamics model of an all-electric bidirectional stabilizer taking the actuator dynamics into account is established,and a nonlinear sliding mode control method based on neural network compensation is proposed.The sliding mode surface and the improved sliding mode robust control law based on hyperbolic tangent function are introduced to construct the nonlinear sliding mode control function,which can effectively eliminate the system oscillations and improve the system stability.Meanwhile,the multilayer neural network technique is deeply fused to accurately estimate the uncertainty in the system and make compensation for the feedforward,thereby avoiding high-gain feedback.It is rigorously demonstrated by the stability theory based on Lyapunov functions that the proposed control strategy can achieve the asymptotic stability performance of tank all-electric bidirectional stabilizer with continuous control inputs.A co-simulation environment and a semi-physical experimental platform are built.The superiority of the proposed control strategy is verified through a large number of comparative experiments.

Key words: tank, all-electric bidirectional stabilizer, sliding mode control, neural network, dynamics modeling, comparative experimental validation

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