A NEURAL STRATEGY FOR NETWORK BASED CONTROL FLEXIBLE-JOINT MANIPULATORS*
Vladimír Zeman, Rajni V. Patel, K. Khorasani, Concordia Universiry · 1989
The increased complexity of the dynamics of current robots with joint or link elasticity makes conventional robot control strategies inappropriate. The standard form of adaptive control does not appear to be applicable since the basic assumptions on the system dynamics and nonlinear characteristics are rarely satisfied. In this paper, we propose a new scheme for robust control of manipulators with flexible joints using neural networks. A multi-layer backpropagation neural network is designed and trained to compute the inverse dynamics of a flexible-joint manipulator. This network is implemented in the feedforward path. The main advantage of our scheme is that we do not require any knowledge about the system dynamics and nonlinear characteristics, and can therefore treat the manipulator as a black box. It is shown that the manipulator must be observable to ensure convergence of the neural net training procedure, and some suggestions for selecting manipulator outputs so as to make it observable are proposed. Simulation results for a single-link flexible-joint manipulator exemplify the performance of the resulting open and closed-loop control systems.