Adaptive Neural Control for Bilateral Teleoperation System using External Force Approach

The paper deals with external force control approach based on four channel scheme, that is reported in previous paper (Outayeb et al., 2016). The problem of controlling bilateral teleoperation system under disturbances due mainly to unknown environment, dynamic robot uncertainties and in presence of noisy measurement of force sensors is considered. The Control Algorithms are obtained on two control strategies, the first one consists on a force/Impedance control approach applied to the master robot, whereas the second one consists on external force control loop combined with position control loop applied to the 3-DOF nonlinear slave robot. A neural network (NN) compensator and online environment estimation based on forgetting factor recursive least squares method (FFRLS) are integrated, to eliminate the effects of uncertainties in dynamic model of the slave robot, as well as, to estimate the unknown time varying characteristics of the environment under noisy measurements of force sensors. Numerical simulations using Labview show the efficacy of proposed scheme to guarantee system stability and acceptable transparency performance.