Repetitive Learning Control of Robotic Manipulators

Abstract

<p>This thesis deals with the repetitive learning control of robotic manipulators. The research topic is motivated by the fact that industrial robots usually perform repetitive tasks. Unlike other conventional controllers, the repetitive learning controller can improve the performance of a robot as it repeats the same task. The proposed control strategy has advantages of easy implementation, low cost and better performance. A critical survey of the state of the art in repetitive learning control of robots is presented. This thesis is divided into two major parts: a two operational modes based approach and a neural network based scheme.</p> <p>In the first part of the thesis, a theoretical framework has been developed for designing controllers based on the proposed two operational modes. The repetitive operations of a robot can be divided into two operational modes: a single operational mode and a repetitive operational mode. Based on this, the author proposes repetitive learning schemes for motion control of both rigid and flexible joints robots, as well as for control of constrained robots with rigid joints. The designed controllers converge in both operational modes. In the single operational mode, the controllers behave like adaptive controllers, while similar to a betterment process in the repetitive operational mode. These conclusions are firmly supported by simulation studies.</p> <p>In the second part of this thesis, special attention is paid to schemes based on neural networks. The author's own modification of back-propagation neural networks is first established in the thesis. It is then applied to motion control of both rigid and flexible joints robots, as well as force control of rigid robots. The neural networks play the role of an approximate inverse dynamic model of a robot and are then parallelled with a conventional PD controller to achieve control goals. Very promising results have been reported in this thesis.</p>