Model Predictive Control for Nonlinear Bilateral Teleoperation Systems With Time Delay

Bilateral teleoperation systems have received extensive attention as a substitute for the human to perform tasks in remote and hazardous areas. This paper proposes a model predictive control strategy for nonlinear bilateral teleoperation systems based on Long-Short Term Memory (LSTM) network. Firstly, model predictive control is used to achieve good tracking performance in the presence of input constraints in the system. At the same time, considering the influence of different proficiency operators operating the master manipulator on the system performance, the error between the actual master manipulator trajectory and the reference trajectory is introduced into a linear feedback term to compensate for the decrease in system control accuracy caused by the error of operators. Secondly, the LSTM network is used to predict the trajectory of the master manipulator in the future time equivalent to the network transmission delay. These predictions are used to control the remote slave manipulator, thereby eliminating the impact of communication delay on the system. Finally, in slave sides, the trajectory creators are applied to generate the desired trajectories to achieve good force feedback performance. Simulation experiments are carried out to verify the proposed control strategy, which can guarantee good performance with both position tracking and force feedback under time delay.