Robotic Trajectory Tracking: Position- and Force-Control

Doctoral thesis (2019)

This thesis employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control. In a first phase, the focus is put on the following of a ... [more ▼]

This thesis employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control. In a first phase, the focus is put on the following of a freeform surface in a discontinuous manner. Next to resulting switching constraints, disturbances and uncertainties, the case of unknown robot models is addressed. In a second phase, once contact has been established between surface and end effector and the freeform path is followed, a desired force is applied. In order to react to changing circumstances, the manipulator needs to show the features of an intelligent agent, i.e. it needs to learn and adapt its behaviour based on a combination of a constant interaction with its environment and preprogramed goals or preferences. The robotic manipulator mimics the human behaviour based on bio-inspired algorithms. In this way it is taken advantage of the know-how and experience of human operators as their knowledge is translated in robot skills. A selection of promising concepts is explored, developed and combined to extend the application areas of robotic manipulators from monotonous, basic tasks in stiff environments to complex constrained processes. Conventional concepts (Sliding Mode Control, PID) are combined with bio-inspired learning (BELBIC, reinforcement based learning) for robust and adaptive control. Independence of robot parameters is guaranteed through approximated robot functions using a Neural Network with online update laws and model-free algorithms. The performance of the concepts is evaluated through simulations and experiments. In complex freeform trajectory tracking applications, excellent absolute mean position errors (<0.3 rad) are achieved. Position and torque control are combined in a parallel concept with minimized absolute mean torque errors (<0.1 Nm). [less ▲]

Robotic trajectory tracking: Bio-inspired position and torque control

in Procedia CIRP (2019)

As far as complex contact-based manufacturing tasks are concerned, humans outperform machines. Indeed, conventionally controlled robotic manipulators are limited to basic applications in close to ideal ... [more ▼]

As far as complex contact-based manufacturing tasks are concerned, humans outperform machines. Indeed, conventionally controlled robotic manipulators are limited to basic applications in close to ideal circumstances. However, tedious work in hazardous environments, make some tasks unsuitable for humans. Therefore, the interest in expanding the application-areas of robots arose. This paper employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control in the presence of uncertainties and switching constraints. The robotic manipulators mimicking the human behavior based on bio-inspired algorithms, take advantage of their know-how. Simulations and experiments validate the concept-performance. [less ▲]

CILAP-Architecture for Simultaneous Position- and Force-Control in Constrained Manufacturing Tasks

in ICINCO 2018 Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics Volume 2 (2018, July)

This paper presents a parallel control concept for automated constrained manufacturing tasks, i.e. for simultaneous position- and force-control of industrial robotic manipulators. The manipulator’s ... [more ▼]

This paper presents a parallel control concept for automated constrained manufacturing tasks, i.e. for simultaneous position- and force-control of industrial robotic manipulators. The manipulator’s interaction with its environment results in a constrained non-linear switched system. In combination with internal and external uncertainties and in the presence of friction, the stable system performance is impaired. The aim is to mimic a human worker’s behaviour encoded as lists of successive desired positions and forces obtained from the records of a human performing the considered task operating the lightweight robot arm in gravity compensation mode. The suggested parallel control concept combines a model-free position- and a model-free torque-controller. These separate controllers combine conventional PID- and PI-control with adaptive neuro-inspired algorithms. The latter use concepts of a reward-like incentive, a learning system and an actuator-inhibitor-interplay. The elements Conventional controller, Incentive, Learning system and Actuator-Preventer interaction form the CILAP-concept. The main contribution of this work is a biologically inspired parallel control architecture for simultaneous position- and force-control of continuous in contrast to discrete manufacturing tasks without having recourse to visual inputs. The proposed control-method is validated on a surface finishing process-simulation. It is shown that it outperforms a conventional combination of PID- and PI-controllers. [less ▲]

Neuro-Inspired Reward-Based Tracking Control for Robotic Manipulators with Unknown Dynamics

in Proceedings of the 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE) (2017, December)

Tracking control for robotic manipulators is required for numerous automation tasks in manufacturing engineering. For this purpose, model-free PD-controllers are largely implemented by default in ... [more ▼]

Tracking control for robotic manipulators is required for numerous automation tasks in manufacturing engineering. For this purpose, model-free PD-controllers are largely implemented by default in commercially available robot arms and provide satisfactory performance for simple path following applications. Ever more complex automation tasks however ask for novel intelligent and adaptive tracking control strategies. In surface finishing processes, discontinuous freeform paths as well as changing constraints between the robotic end-effector and its surrounding environment affect the tracking control by undermining the stable system performance. The lacking knowledge of industrial robot dynamic parameters presents an additional challenge for the tracking control algorithms. In this paper the control problem of robotic manipulators with unknown dynamics and varying constraints is addressed. A robust sliding mode controller is combined with an RBF (Radial Basis Function) Neural Network-estimator and an intelligent, biomimetic BELBIC (Brain Emotional Learning-Based Intelligent Control) term to approximate the nonlinear robot dynamics function and achieve a robust and adaptive tracking performance. [less ▲]

PID and Biomimetic Variable Structure Path Tracking Control in Automated Surface Finishing Processes

in Robotix-Academy Conference for Industrial Robotics (RACIR) 2017 (2017)

This paper addresses freeform surface following control as one of the main challenges in automating surface finishing processes. Successive changes in constraints between the tool attached to the robotic ... [more ▼]

This paper addresses freeform surface following control as one of the main challenges in automating surface finishing processes. Successive changes in constraints between the tool attached to the robotic manipulator and its surroundings are due to complex workpiece-geometries and result in a switched nonlinear system. The control problem of the latter is addressed by industrial state-of-the-art conventional PID control as well as by biomimetic variable structure control which are both applied to a benchmark path tracking problem as characteristically encountered in surface finishing. [less ▲]

BELBIC-Sliding Mode Control of Robotic Manipulators with Uncertainties and Switching Constraints

in Proceedings of the ASME 2016 International Mechanical Engineering Congress and Exposition (2016, November)

This paper addresses the control problem for trajectory tracking of a class of robotic manipulators presenting uncertainties and switching constraints using a biomimetic approach. Uncertainties, system ... [more ▼]

This paper addresses the control problem for trajectory tracking of a class of robotic manipulators presenting uncertainties and switching constraints using a biomimetic approach. Uncertainties, system-inherent as well as environmental disturbances deteriorate the performance of the system. A change in constraints between the robot’s end-effector and the environment resulting in a switched nonlinear system, undermines the stable system performance. In this work, a robust adaptive controller combining sliding mode control and BELBIC (Brain Emotional Learning-Based Intelligent Control) is suggested to remediate the expected impacts on the overall system tracking performance and stability. The controller is based on an interplay of inputs relating to environmental information through error-signals of position and sliding surfaces and of emotional signals regulating the learning rate and adapting the future behaviour based on prior experiences. The proposed control algorithm is designed to be applicable to discontinuous freeform geometries. Its stability is proven theoretically and a simulation, performed on a two-link manipulator verifies its efficacy. [less ▲]