A Neural-network Based Observer for Flexible-joint Manipulators

The problem of designing a nonlinear observer for flexible-joint manipulators using a neural network approach is considered in this paper. In the first instance, no a priori knowledge about the system dynamics is assumed in developing the basic structure of the neural observer. The recurrent neural network configuration is obtained by a combination of a multilayer feedforward network and dynamical elements in the form of stable filters. Next, partial knowledge about the manipulator dynamics is assumed. However, a model of the joint stiffness, stiction, and friction is assumed to be unknown. This modification greatly simplifies the original design and facilitates its real-time implementation. This scheme does not need any measurement from the output shaft of the manipulator. The neural networks are trained online. Simulation results for single and two-link manipulators are presented to demonstrate the effectiveness of the proposed approach.