Adaptive Control Of Robot Manipulators Using CNN Under Actuator Constraints

In this paper, a stable neuro adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamic equations of the robot and measurements of joint positions only. The gravity torque which may include payload variation and disturbances etc represent system uncertainty, which is estimated by a single layer Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using CNN to estimate the gravity torque. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload parameter variation but also to unstructured one such as disturbances. The validity of the control scheme is shown by simulation studies on a two link robot manipulator.