Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on s...Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.展开更多
The vibration control in the frequency domain is significant.Therefore,an active vibration control in frequency domain is studied in this paper.It is generally known that piezo-intelligent structures possess satisfact...The vibration control in the frequency domain is significant.Therefore,an active vibration control in frequency domain is studied in this paper.It is generally known that piezo-intelligent structures possess satisfactory performances in the area of vibration control,and macro-fiber composites(MFCs)with high sensitivity and deformability are widely applied in engineering.So,this paper uses the MFC patches and designs a control method based on the pole placement method,and the natural frequency of the beam can be artificially designed.MFC patches are bonded on the top and bottom surfaces of the beam structure to act as the actuators and sensors.Then,the finite element method(FEM)is used to formulate the equation of motion,and the pole placement based on the out-put feedback method is used to design the active controller.Finally,the effectiveness of the active control method is verified.展开更多
文摘Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.
基金supported by the National Natural Science Foundation of China(Nos.11802069,11761131006)the China Postdoctoral Science Foundation(No.3236310534)+1 种基金the Heilongjiang Provincial Postdoctoral Science Foundation(Nos.002020830603,LBHTZ2008)the China Fundamental Research Funds for the Central Universities(No.GK2020260225).
文摘The vibration control in the frequency domain is significant.Therefore,an active vibration control in frequency domain is studied in this paper.It is generally known that piezo-intelligent structures possess satisfactory performances in the area of vibration control,and macro-fiber composites(MFCs)with high sensitivity and deformability are widely applied in engineering.So,this paper uses the MFC patches and designs a control method based on the pole placement method,and the natural frequency of the beam can be artificially designed.MFC patches are bonded on the top and bottom surfaces of the beam structure to act as the actuators and sensors.Then,the finite element method(FEM)is used to formulate the equation of motion,and the pole placement based on the out-put feedback method is used to design the active controller.Finally,the effectiveness of the active control method is verified.
