Smith Predictor Type Control Architectures for Time Delayed Teleoperation

Abstract

An early control methodology for time delayed plants is the Smith predictor, in which the plant model is utilized to predict the non-delayed output of the plant and move the delay out of the control loop. Recent Smith predictor based teleoperation control architectures have used linear or fixed-parameter dynamic approximations of the slave/environment at the master for environment contact prediction. This paper discusses and analyzes the performance of the previous work and proposes new architectures to overcome their shortcomings. The proposed architectures consist of a novel pseudo two-channel nonlinear predictive controller and its variations that use neural networks for online estimation of the slave and environment dynamics to replicate the environment contact force at the master using a similar local network. Intermittent contact experiments are conducted on a teleoperation test-bed consisting of two Planar Twin-Pantograph haptic devices. The experimental results with half a second delay demonstrate significant improvement in stability and performance by the proposed neural network based predictive control architectures over traditional force-position and linear Smith predictor based control architectures.