Fuzzy neural network control for mechanical arm based on adaptive friction compensation

When tracking the trajectory of the mechanical arm in a joint space, the system is affected by friction non-linearity, unknown dynamic parameters and external disturbances that makes it difficult to improve the control accuracy of the mechanical arm. To solve the above problems, this paper introduces LuGre friction model and designs a new joint space trajectory tracking controller based on the adaptive fuzzy neural network. The controller is capable to make the adaptive adjustment of the center and width of the basis function, can approach the nonlinear link having the LuGre friction on line, and uses the sliding mode control term to reduce the approximation error. The introduction of LuGre model into the mechanical arm system can more truly simulate the friction link of the system, which is of great significance to the high precision control of the mechanical arm. The Lyapunov method is used to prove the stability of the closed-loop system. The simulation results show that the designed adaptive fuzzy neural network can effectively compensate the non-linear links including friction without precise system parameters, and the controller has strong robustness to load changes, thus realizing high-precision trajectory tracking of the mechanical arm in joint space