Control of industrial manipulator vibration using artificial neural networks
This research project is a study of the application of artificial neural networks to the control of robot manipulator vibration, specifically concentrating on the improvement of dynamic path accuracy during linear motion. The aim of the investigation was to achieve this through compensation of the inherent vibration experienced at the end effctor. The first phase of the project was concerned with the mechanical structure of the manipulator with the specific aim of attaining a comprehensive understanding of robot manipulator vibration. This was achieved through the formaulation of a mathematical model using Lagrangian mechanics and through an empirical description, obtained through the implementation of laser interferometry and experimental modal analysis. The laser interferometry data were used to produce an empirical model, using an artificial neeural network (ANN) architecture, to learn to predict the vibration experienced at the end-effector. The second phase of the project concentrated on the simulation and design of an ANN based active vibration compensation system. The ANN architecture used was a time delay Elman network and the learning algorith used was a modified stochastic/backpropagation strategy. In simulation the controller was able to realise a reduction in vibration of 83.6 percent after training. After implementing the controller on a PUMA562C industrial manipulator a modest reduction of 16 per cent was achieved. The considerably lower magnitude of reduction was caused by the under-specified design of mechanical actuator Finally to assess the performance characteristics of the ANN based controller an analysis of the adaptive capabilities of the system to slowly changing manipulator characteristics and a comparison of the systems capabilities with a digital PID phase inverse controller applied to the control problem were investigated. The ANN based controller was capable of providing limited reakl-time adaption due to continuous reinforcement feedback. The ANN controller produced ann additional 6 percent reduction in vibration over the PID controller, despite a significantly lower sampling rate.