Physics

Engineering

Artificial intelligence

Joint (building)

Control (management)

Link (geometry)

Observer (physics)

Computer network

Computer science

Quantum mechanics

Structural engineering

Robot

IEEE Transactions on Neural Networks and Learning Systems

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Observer-Based Neural Control of <i>N</i>-Link Flexible-Joint Robots

Observer-Based Neural Control of N-Link Flexible-Joint Robots