Adaptive fuzzy visual tracking control for manipulator with quantized saturation input

• DOI: 10.1007/s11071-017-3513-2
• Publication Date: 2017-05-05T15:51:59Z
• Authors: Fujie Wang; Zhi Liu; Yun Zhang; C. L. Philip Chen
• Keywords: Visual Servoing; Observer (physics)

This paper investigates the adaptive fuzzy visual tracking control problem for telecontrolled manipulator system with input quantized by the proposed saturation switch quantizer (SSQ). Compared with the existing logarithmic quantizer and uniform quantizer, the major superiority of this newly SSQ lies in its adjustable communication rate and quantization density, simultaneously taking the input saturation effect into account. By establishing a nonlinear decomposition-based scheme for the output of SSQ, the control difficulty caused by discrete quantized input is overcome successfully. In addition, the requirement of visual velocity in controller construction is removed by introducing a visual velocity observer, and thus, large image noises and computational burden are both avoided. Subsequently, without the exact knowledge of robot dynamics, a novel adaptive fuzzy visual servoing controller is developed to guarantee the boundedness of closed-loop signals and the tracking performance. The effectiveness of the proposed adaptive fuzzy control scheme is confirmed by comparative simulations.