Neural-adaptive control of robotic manipulators using a supervisory inertia matrix

This paper utilizes a novel neural-adaptive method for controlling a two-link robotic manipulator. We do not need to resort to estimating the inverse dynamics. Our control utilizes the full dynamic model estimate including an inertia matrix estimate, referred to as a forward dynamics approach. Our novel contribution is to use an inertia matrix estimate to supervise the training of the neural networks. We find this overcomes the practical difficulties typically encountered with the forward dynamics method. The proposed method greatly improves performance over the forward dynamics approach, verified in experiment. The method is robust to changes in the real inertia matrix, because of a payload, even though the supervisory inertia matrix remains constant.