Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System

Taking an intelligent trimming device hydraulic manipulator as the research object, aiming at uncertainty, nonlinearity and complexity of its system, a trajectory tracking control scheme is studied in this paper. In light virtual work principle, coupling dynamic model system established. order to improve anti-interference adaptive abilities compound strategy combining neuro-fuzzy inference (ANFIS) proportional integral derivative (PID) controller proposed. The neural learning algorithm utilized train given input output data adjust membership functions fuzzy then PID parameters can be adjusted adaptively accomplish tracking. Based on MATLAB/Simulink, simulation addition, prove effectiveness ANFIS-based (ANFIS-PID), performance compared with (FPID) controllers. results indicate that ANFIS-PID superior other controllers effect precision, provides more accurate effective method for agriculture.