Electrical engineering

Physics

Control engineering

Trajectory

Torque

Mobile robot

Agronomy

Biology

Optics

Admittance

Astronomy

Engineering

Adaptation (eye)

Artificial intelligence

Controller (irrigation)

Electrical impedance

Control (management)

Observer (physics)

Computer science

Quantum mechanics

Control theory (sociology)

Artificial neural network

Robot control

Robot

Thermodynamics

Radial basis function

IEEE Transactions on Cybernetics

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.

Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot–Environment Interaction