Teleoperation Control of a Position‐Based Impedance Force Controlled Mobile Robot by Neural Network Learning: Experimental Studies

Abstract

Effective haptic performance in teleoperation control systems can be achieved by solving two major problems: the timedelay in communication channels and the transparency of force control. The time-delay in communication channels causes poor performance and even instability in a system. The transparency of force feedback is important for an operator to improve the performance of a given task. This article suggests a possible solution for these two problems through the implementation of a teleoperation control system between the master haptic device and the slave mobile robot. Regulation of the contact force in the slave mobile robot is achieved by introducing a position-based impedance force control scheme in the slave robot. The time-delay problem is addressed by forming a Smith predictor configuration in the teleoperation control environment. The configuration of the Smith predictor structure takes the time-delay term out of the characteristic equation in order to make the system stable when the system model is given a priori. Since the Smith predictor is formulated from exact linear modeling, a neural network is employed to identify and model the slave robot system as a nonlinear model estimator. Simulation studies of several control schemes are performed. Experimental studies are conducted to verify the performance of the proposed control scheme by regulating the contact force of a mobile robot through the master haptic device.

A model reference adaptive variable impedance control method for robot

“…Although fuzzy control has strong robustness and fault tolerance, simple fuzzy processing of information will lead to reduction of control accuracy and deterioration of dynamic quality of the system. Literature [6,7] adopt neural network control method to achieve force tracking control, but this method is usually designed complex and it takes several weeks or even longer to training data. Adaptive control is widely used because it does not require sample training, and can adjust impedance control parameters in real time according to force errors.…”

Simpsons 1/3 base fuzzy neural network with passive controller of wheeled mobile robot system

“…A Teleoperation based WMR is more useful when robot tracking can enhance navigation and wheel slippage. In many scenarios, a human partner can control the mobile robots through a teleoperation interface to perform a collaborative task (Choi & Jung, 2020;Cooke, 2006;Tunc, 2010;Tunc & Tunc, 2011;. In particular, teleoperation is utilized for workspace mismatch; a matching process is carried out between the master and the slave robot wheel velocity.…”

“…The force reflection in the slave robot should be regulated and transferred to the master side to maintain stability, which is solved by the backstepping control method. In (Choi & Jung, 2020) discussed neural network learning technique is applied in position-based force controller in the framework of a haptic device. The mobile robot contact force is made by a haptic device of bilateral transparency system: the contact force of the slave robot is controlled by the position-based master robot through force feedback.…”

Predictive display for teleoperation with virtual reality fusion technology

“…Teleoperation brings the benefits of remote control and manipulation to distant locations or harsh environments. The system allows operators to send commands from a remote console, traditionally called the master device, to a robot, traditionally called the slave device, and provides motion synchronization [1]. Telerobotics is perhaps one of the earliest aspects of robotics.…”