Identification and Control of Flexible Joint Robot Using Multi-Time-Scale Neural Network

In this paper, a new identification and control scheme for the flexible joint robotic manipulator is proposed. Firstly, by defining some new state variables, the commonly used dynamic equations of the flexible joint robotic manipulators are transformed into the standard form of a singularly perturbed model. Subsequently, an optimal bounded ellipsoid algorithm based identification scheme using multi-time-scale neural network is proposed to identify the unknown system dynamic equations. Lastly, by using the singular perturbation theory, an indirect adaptive controller based on the identified model is proposed to control the system such that the joint angles can track the given reference signals. The closed-loop stability of the whole system is proved, and the effectiveness of the proposed schemes is verified by simulations.