Adaptive Control of Space Robot Manipulators with Task Space Base on Neural Network
Abstract: As are considered,
the body posture is controlled and position cannot control, space manipulator system
model is difficult to be set up because of disturbance and model uncertainty.
An adaptive control strategy based on neural network is put forward. Neural
network on-line modeling technology is used to approximate the system uncertain
model, and the strategy avoids solving the inverse Jacobi matrix, neural network
approximation error and external bounded disturbance are eliminated by variable
structure control controller. Inverse dynamic model of the control strategy
does not need to be estimated, also do not need to take the training process,
globally asymptotically stable of the closed-loop system is proved based on the
lyapunov theory. The simulation results show that the designed controller can
achieve high control precision has the important value of engineering
application.
Author: Zhou Shuhua
Journal Code: jptkomputergg140071
