Acceleration based learning control of robotic manipulators using a multi-layered neural network

This paper presents a nonlinear compensation method based on neural networks for trajectory control of robotic manipulators. A multi-layered perceptron neural network (MLP) is used to predict the required actuator torques of a robot to follow a desired trajectory, and these predicted torques are applied to the robot as feedforward compensations in parallel to a linear feedback controller. An acceleration based learning scheme is proposed to adjust the connection weights in the neural network to form an approximated dynamic model of the robot. Simulation results show that the proposed learning scheme improves the speed of error convergence of the system and reduces the convergent error with the efficient adaptation to the changing system dynamics. The validity of the proposed learning scheme is verified through experiments