Synchronous Tracking of Bilateral Teleoperation Robots under Uncertainties and Time Delays: An Adaptive Neural Network Approach with Event-Triggered Mechanisms

This paper introduces an adaptive neural network control strategy for bilateral teleoperation systems with uncertainties and delays, adaptable to different event-triggering mechanisms. By introducing an event triggering mechanism, the system reduces the frequency of network communication, and the master-slave robots only transmit signals when conditions are met. This approach ensures a non-zero interval between trigger events and proves, through the Lyapunov principle, the asymptotic convergence of position synchronization tracking errors under different event-triggering conditions. Eventually,the viability and effectiveness of the designed control strategy are validated by simulation experiments.