Robust fuzzy model-following control of robot manipulators

A robust fuzzy model-following control system is proposed for the control of robot manipulators. The application field to -link robot manipulators with torque disturbance and measurement noise is addressed. The control objective is obtained by tailoring a nominal adaptation process of parameters to implement appropriate function approximation and facilitating a self-tuning mechanism on the consequent membership functions to overcome the equivalent uncertainty. The major differences comparing with previous adaptive fuzzy control approaches are that a novel fuzzy system with self-tuning mechanism provided robust property and the rulebase in the form of “IF situation THEN the control input” rather than “IF situation THEN the value of some nonlinear function of the robot” are introduced. The proposed multilayer fuzzy logic controller can improve both transient and stability margins without a priori knowledge about the dynamic model or parameters of the robotic system. Using the Lyapunov stability method, the uniform ultimate boundedness of tracking error has been proved. The performance is demonstrated by simulating the control of a two-link robot in various situations.