Time delay compensated disturbance observer-based sliding mode slave controller and neural network model for bilateral teleoperation system

With the advancement of robotics, mechatronic systems, and automation systems, bilateral teleoperation systems are utilized for performing tasks in remote environments based on commands provided by the master. In application domains like drilling, space operations, medical surgery, undersea exploration, and several other areas, remote task operations are performed using teleoperation systems. Good transparency based on the force feedback and position tracking is still challenging tasks among conventional teleoperation systems. Hence, in order to overcome the challenges, radial basis function neural network (RBFNN) and sliding mode slave teleoperation controller-based disturbance observer (SMSTC-DOB) are proposed in this research. Here, the role of the RBFNN is to estimate the environment parameter for the desired trajectory planning. Besides, the SMSTC-DOB-based slave design helps to synchronize the performance between the slave and master for obtaining stability and good transparency by considering issues like nonlinearities, uncertainties, passivity, and time delay. The implementation is employed in MATLAB/Simulink, which depicts the better transparency of the model in terms of force feedback and position tracking.