Operating system

Agronomy

Backstepping

Biology

Computer science

Quantum mechanics

Engineering

Adaptive control

Actuator

Controller (irrigation)

Control (management)

Artificial intelligence

Exponential stability

Transient (computer programming)

Control theory (sociology)

Artificial neural network

Physics

Control engineering

Nonlinear system

Tracking error

IEEE Transactions on Systems Man and Cybernetics Systems

Hydraulic manipulators are extremely complicated systems due to the highly nonlinear characteristics, strong coupling among multiple joints, and heavy modeling uncertainties, which greatly complicate the high-performance tracking controller development compared with conventional manipulators driven by electrical motors. To overcome the above obstacles, this article proposes a novel radial basis function neural network (RBFNN)-based adaptive asymptotic prescribed performance controller for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -degrees of freedom (DOF) hydraulic manipulators. First, the entire manipulator system model containing actuator dynamics is derived. Then, the adaptive asymptotic prescribed performance controller is synthesized based on the backstepping framework, in which the RBFNN is employed to estimate the unknown joint coupling dynamics, while the RBFNN reconstruction er...

Neural Network-Based Adaptive Asymptotic Prescribed Performance Tracking Control of Hydraulic Manipulators