The control algorithm for compliant robotic tasks based on neuro-genetic approach

In this paper, a systematic connectionist controller design approach is proposed to guarantee stability and desired performance of the robotic system for compliant tasks by effectively combining genetic algorithms (GA) with neural classification and neural learning control techniques. The effectiveness of the approach is shown by using a simple and efficient decimal and binary GA optimization procedures to tune and optimize the performance of a neural classifier and controller, together with tuning of feedback controller. In order to demonstrate the effectiveness of the proposed GA approach, some compliant motion simulation experiments with robotic arm placed in contact with dynamic environment have been performed.