Control of Robotic Arm Based on TSK Fuzzy Model and Hierarchical Genetic Algorithms

Abstract

This article introduces a novel design of Takagi-Sugeno-Kang (TSK) fuzzy control of robotic arm based on hierarchical genetic algorithms (HGAs). First, the selection of a function matrix Q(x) causes the nonlinearity of the model uncertainty and external disturbances to be trivial. Second, the nonlinear dynamic equation of the robotic arm is described by TSK fuzzy model. Based on the Lyapunov method, the linear matrix inequalities (LMI) can promise the TSK control system is stable. The optimal membership functions and control gains can be simultaneously explored the optimal values by HGAs. Last, the proposed HGAs-based LMI is applied to design parallel distributed compensation (PDC) so that the robotic arm can follow the command signal.