A new learning controller based on neural networks for robot trajectories tracking

This paper presents a new learning control method for robot trajectory tracking of unknown model system based on both the inverse dynamics neural network identifier and feedforward compensation network controller. Basic control configurations are briefly presented and new online training schemes are proposed. Some measurements to accelerate the learning and to improve the stability and convergence are discussed. It is also shown that the method can follow and arbitrarily prescribe trajectories. Simulation was performed to show the feasibility and effectiveness of the proposed scheme