Adaptive Tracking Control with Uncertainty-aware and State-dependent Feedback Action Blending for Robot Manipulators

Adaptive control can significantly improve tracking performance of robot manipulators subject to modeling errors in dynamics. In this letter, we propose a new framework combining the composite adaptive controller using a natural adaptation law and an extension of the adaptive variance algorithm (AVA) for controller blending. The proposed approach not only automatically adjusts the feedback action to reduce the risk of violating actuator constraints but also anticipates substantial modeling errors by means of an uncertainty measure, thus preventing severe performance deterioration. A formal stability analysis of the closed-loop system is conducted. The control scheme is experimentally validated and directly compared with baseline methods on a torque-controlled KUKA LWR IV+

An Adaptive Control Approach with Automatic Gain Blending for Robot Manipulators under Dynamic Uncertainties

To ensure convergence of the tracking errors despite uncertainties in the inertial parameters of a robot manipulator, adaptive tracking controllers can be applied, which adapt a parameter estimate on-line. The common adaptive robot controllers feature a proportional-derivative (PD) feedback of the tracking errors. Tuning the PD gains can be challenging due to tradeoffs between desired control performance and the risk of exceeding feasible limits of actuators. Far from the desired equilibria, small gains are beneficial in order to avoid actuator saturation. However, low gains usually come at the cost of a degraded tracking performance, especially in the presence of model uncertainties. The thesis addresses the problem in proposing a combined adaptive tracking controller for robot manipulators. It adapts a parameter estimate utilizing a natural adaptation law. Moreover, nominal high gains and global low gains are adaptively blended depending on the current tracking errors and the estimated uncertainties. In general, the blended gains decrease with growing tracking errors. However, they are shifted towards the nominal high gains if the errors remain large or if model uncertainties are estimated to affect the control performance. Consequently, the current weighting between ensuring a desired control performance and avoiding large actuation effort is automatically adapted. A recently proposed adaptation mechanism that can blend two control actions depending on the current tracking errors is taken as a starting point for the controller development. The thesis extends the approach such that the current estimated uncertainties can be included and unifies it with a parameter adaptation law to obtain a combined adaptive controller. A mathematical proof for the global uniform convergence of the tracking errors and the boundedness of the parameter estimation error is provided. Simulative and experimental results with a lightweight robot manipulator with seven degrees of freedom validate the theoretic results

The MEADOW Guidelines

L'ouvrage doit être cité de la façon suivante:MEADOW Consortium (2010), The MEADOW Guidelines, Project funded within the 6th Framework Programme of the European Commission’s DG Research, Grigny, France, http://www.meadow-project.eu/index.php?/Article-du-site/Guidelines.htmlInternational audienceThe MEADOW Guidelines propose a measurement framework for collecting and interpreting internationally harmonised data on organisational change and its economic and social impacts for both private and public sector organisations. Reliable harmonised statistics on organisational change would provide the basis for effective benchmarking through the exchange of information on best practices across EU-member states and in this way could contribute directly to the success of European policy initiatives aimed at increasing the fl exibility and adaptability of organisations and employees while simultaneously improving the quality of jobs during economic booms as well as downturns. The MEADOW project (MEAsuring the Dynamics of Organisations and Work) is a European Commission funded Coordinating Action that brought together a multidisciplinary consortium of 14 partners from 9 European countries. The Meadow consortium has been actively supported by a number of key European and international institutions with central responsibilities for data collection and dissemination, including the OECD, EUROSTAT, the European Foundation for the Improvement of Living and Working Conditions, the European Agency for Safety and Health at Work, and DG Employment. MEADOW has been funded by the European Commission under priority 7 (Citizens and Governance) of the 6th RTD Framework Program.Les lignes directrices MEADOW proposent un cadre de mesure pour la collecte et l'interprétation de données harmonisées au niveau international sur le changement organisationnel et ses conséquences économiques et sociales pour les organisations des secteurs public et privé. Des statistiques harmonisées fiables sur le changement organisationnel fourniraient la base d'analyses comparatives permettant d’identifier les bonnes pratiques au sein des États membres de l'UE et de cette manière pourrait contribuer directement au succès des initiatives politiques européennes visant à accroître la flexibilité et l'adaptabilité des organisations et de leurs salariés tout en améliorant la qualité des emplois aussi bien en période d'expansion que de contraction de l’activité économique. Le projet MEADOW (mesurer la dynamique des organisations et du travail) est une action de coordination financée par la Commission européenne qui a réuni un consortium pluridisciplinaire de 14 partenaires issus de 9 pays européens. Le consortium MEADOW a été activement soutenu par des institutions européennes et internationales ayant des responsabilités centrales dans la collecte et la diffusion des données, comme l'OCDE, EUROSTAT, la Fondation européenne pour l'amélioration des conditions de vie et de travail, l'Agence européenne pour la sécurité et la santé au travail, et la DG emploi. MEADOW a été financé par la Commission européenne au titre de la priorité 7 (Citoyens et gouvernance) du 6ème programme-cadre de Recherche et développement