Research on Uncalibrated Visual Servo Based on GWO-ELM Algorithm and Fuzzy Control
To address the problems of slow operation of traditional image-based visual servo system and the impact of calibration accuracy on the solution of image Jacobi matrix,this paper proposes a visual servo control method based on the combination of gray wolf optimized extreme learning machine(GWO-ELM)and fuzzy control.The method uses the gray wolf algorithm(GWO)to optimize the initial weights of the ELM model to increase the stability of the model,estimates the image Jacobi matrix pseudo-inverse to pre-dict the end motion speed of the robot arm,and then introduces the fuzzy control(Fuzzy Control)to design the visual servo controller to build a calibration-free visual servo control system and conducts the experi-ments on the machine.The experimental results show that the operating efficiency of Fuzzy Control-GWO-ELM-IBVS is improved compared with that of GWO-ELM-IBVS,positioning errors can be controlled with-in specified thresholds,which verifies the effectiveness of the calibration-free visual servo control system proposed in this thesis.
image Jacobi matrixgray wolf algorithm optimized extreme learning machinefuzzy control
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