Learning Control of Industrial Robots Interacting with Dynamic Environment by Application of Multilayer Perceptrons

摘要

The major concern of this paper is the application of nonrecurrent and recurrent connectionist architectures for fast on-line learning of robot dynamic uncertainties which are used at the executive hierarchical control level in the case of robot contact tasks. The connectionist structures are integrated in the non-learning "stabilization" and impedance control laws for contact tasks which enable simultaneous stabilization and good tracking performance of position and force. It has been shown that the problem of tracking a specified reference trajectory and specified force profile with a preset quality of their transient response can be efficiently solved by means of application of the mulyilayer perceptrons. Some simulation results of deburring proces with robot MANUTEC r3 are shown to verify effectiveness of the proposed control learning algorithms.