Neural Network Adaptive Force and Motion Control of Robot Manipulators in the Operational Space Formulation

Abstract

It is well-established that dynamically compensated (model-based) force / motion controller strategy provides better performance than the standard Proportional - Integral - Derivative (PID) controller. However, the dynamic model and parameter values, especially for a real robot, are very difficult to identify precisely. Therefore a fast and cost-effective adaptive method is highly desired.<br><br>The main objective in this thesis deals ultimately with the Neural Network (NN) adaptive control for parallel force and motion in the operational space formulation. The operational space formulation, capable of providing unified force motion control and tracing contoured surface without the need for the knowledge of the surface geometry, is selected as the working platform. In this thesis, all the proposed neuro-adaptive control strategies were constructed in operational space formulation.<br><br>The development of this thesis is presented in incremental manner: (1) motion only neuro-adaptive control, (2) motion only neuro-adaptive control with velocity observer (since our physical robot does not have a joint velocity feedback), (3) force and motion neuro-adaptive control which, and accompanied by (4) neuro-adaptive impact force control.<br><br>All the proposed strategies assume no prior knowledge of the robot dynamics where the NN weights were initialized with zero. Lyapunov stabilities showing bounded stability of the tracking errors and NN weight errors were also provided for all the proposed strategies. The proposed strategies were not only shown to be stable in real-time implementation on PUMA 560, but also produced comparable performances to those of the well-tuned inverse dynamics control strategies.