Passive Motion Control for Industrial Robot Arm and Teleoperation System

This thesis mainly addresses issues related to passive motion and its necessity for various applications of industrial robots. It is motivated by the increasing need to have more flexible and cost effective way of changing existing original versions of industrial servo controllers with minimal effort and cost. And further, by doing so, robots are improved with the flexibility, safety and robustness of factory automation systems in uncertain work environments. The thesis mainly focuses on two issues: 1) Flexible motion against passive contact forces from an external environment acting on existing general purpose industrial type robot arms, and 2) Analyze the existing technologies against the force compliance passive motion control (alias of Force-free control-FFC) and its extensions, which are more emphasized based on applications oriented total behavior simulations under real robot parameters and conditions. Theoretical derivations and model development of the common industrial mechatronic servo systems including their application constrains are also provided with appropriate explanations. The dynamic and kinematic attributes of different versions of force compliance passive motion controllers are explained in details with required mathematical models and also provided the explicit proofs of derived equations. A considerable weight is given to an application of teleoperation, based on existing industrial type robots. The proposed approach vigorously highlighted that needless of major hardware or costly original factory fitted control architecture changes. In addition to that fault tolerance aspects are considered. Moreover, loses and delays during communication of data through networks are also illustrated in places which they are underlying, and with faithful experiments. The core of the entire research is focused based on the force compliance passive motion control (FCPMC or FFC) technique and its derivatives. However, the above technique is derived based on the dynamic compensations of gravity and friction forces combined with inverse characteristics of the industrial type servo controller and parameters. Therefore, research is again extended to establish the above controller based on twofold intelligent controller representations. Initially, a Gaussian neural network model representation is introduced to eliminate the errors incurred due to uncertainty of parameter estimations (eg: interference dynamic effect of multi-link industrial articulated platforms) in original model based analytical approach. Then, the Gaussian network is also further improved by means of initial state of the weights determination fortified the authenticity of Gaussian-neural network implementation, hence, an industry expertise heuristics being adapted during initial estimations of weights to govern the correct performance of the controller. Besides, one of the early development models of force-free control by independent compensation was derived by confining the speed of the robot movements to “very low” thus, such assumption was questioned and argued. This thesis also provides the algebraic analytical solution to the subjective issue and for which Genetic Algorithm (GA) is used to evaluate optimal values for coefficients. Throughout the research, experiments were carried out by using a Performer MK3s industrial type robot and for all simulations were also carried out based on same real robot parameters.