Adaptive Control of Flexible Joint Robots Derived from Arm Energy Considerations

Almost all industrial robots exhibit joint flexibility due to mechanical compliance of their gear boxes. In this paper we outline a design of an adaptive controller for flexible joint robots based on the arms energy. The desired actuator trajectory in a flexible joint robot is dependent not only on the desired kinematic trajectory of the link but also on the link dynamics. Unfortunately, link dynamic parameters are unknown in most cases, as a result the desired actuator trajectory is also unknown. To overcome this difficulty, a number of control schemes have suggested the use of acceleration and link jerk feedback. In this paper we describe a control scheme which does not use link jerk or acceleration. The control law we derive is based on the energy of the arm deviating from the desired trajectory and it has two stages with two corresponding adaptation laws. The first stage drives the actuator and the joints to a desired manifold, the second controller then seeks to drive the joints to their desired trajectory. On application of our first controller there is an apparent structural reduction of the order of the system. This apparent reduction in the structure is exploited by our second stage controller. Our control scheme does not require link acceleration or jerk measurements, and the numerical differentiation of the velocity signal, or the inversion of the inertial matrices are also unnecessary. Simulations are presented to verify the validity of the control scheme. The superiority of the proposed scheme over existing rigid robot adaptive schemes is also illustrated through simulation

An adaptive control algorithm for variable stiffness antagonistic joints

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2014. 2. 박종우.In this thesis, we consider the problem of estimating nonlinear stiffness of flexible transmissions in single link robots with antagonistic variable stiffness actuation. Joint stiffness estimation is obtained using an adaptive control algorithm. For the joint stiffness estimation, we assume that all rigid body dynamic parameters of robot except stiffness are known value. The motor position, velocity, link angle position, velocity and flexibility torque are assumed to be measurable for the state-feedback. An adaptive control algorithm with input-output linearization state feedback is used in our problem on the basis that this algorithm is optimal for our problem. Joint stiffness value is assumed to be intrinsically a nonlinear polynomial function of the deformation. Simulation results from performed of single link arm robots are reported, showing a good performance in trajectory tracking of link angle position and in estimating a nonlinear polynomial function of the joint stiffness.1. Introduction 2. Dynamic modeling 3. Adaptive variable stiffness control algorithm 3.1 Review of linearized techniques 3.2 Adaptive control of the SISO system 4. Simulation 4.1 Simulation Setup 4.2 Simulation Results 5 ConclusionMaste

A family of asymptotically stable control laws for flexible robots based on a passivity approach

A general family of asymptotically stabilizing control laws is introduced for a class of nonlinear Hamiltonian systems. The inherent passivity property of this class of systems and the Passivity Theorem are used to show the closed-loop input/output stability which is then related to the internal state space stability through the stabilizability and detectability condition. Applications of these results include fully actuated robots, flexible joint robots, and robots with link flexibility

Control of flexible joint robotic manipulator using tuning functions design

The goal of this thesis is to design the controller for a single arm manipulator having a flexible joint for the tracking problem in two different cases. A controller is designed for a deterministic case wherein the plant parameters are assumed to be known while another is designed for an adaptive case where all the plant parameters are assumed to be unknown. In general the tracking problem is; given a smooth reference trajectory, the end effector has to track the reference while maintaining the stability. It is assumed that only the output of the manipulator, which is the link angle, is available for measurement. Also without loss of generality, the fast dynamics, that is the dynamics of the driver side of the system are neglected for the sake of simplicity; In the first case, the design procedure adopted is called observer backstepping. Since the states of the system are unavailable for measurement, an observer is designed that estimates the system states. These estimates are fed to the controller which in turn produces the control input to the system; The second case employs a design procedure called tuning functions design. In this case, since the plant parameters are unknown, the observer designed in case one cannot be used for determining the state estimates. For this purpose, parameter update laws and filters are designed for estimation of plant parameters. The filters employed are k-filters. The k-filters and the parameter update laws are given as input to the controller, which generates the control input to the system; For both cases, the mathematical models are simulated using Matlab/Simulink, and the results are verified

Experimental External Force Estimation Using a Non-Linear Observer for 6 axes Flexible-Joint Industrial Manipulators

This paper proposes a non-linear observer to estimate not only the state (position and velocity) of links but also the external forces exerted by the robot during Friction Stir Welding (FSW) processes. The difficulty of performing this process with a robot lies in its lack of rigidity. In order to ensure a better tracking performance, the data such as real positions, velocities of links and external forces are required. However, those variations are not always measured in most industrial robots. Therefore, in this study, an observer is proposed to reconstruct those necessary parameters by using only measurements of motor side. The proposed observer is carried out on a 6 DOF flexible-joint industrial manipulator used in a FSW process.ANR-2010-SEGI-003-01-COROUSSO, French National Agenc

Stanford Aerospace Research Laboratory research overview

Over the last ten years, the Stanford Aerospace Robotics Laboratory (ARL) has developed a hardware facility in which a number of space robotics issues have been, and continue to be, addressed. This paper reviews two of the current ARL research areas: navigation and control of free flying space robots, and modelling and control of extremely flexible space structures. The ARL has designed and built several semi-autonomous free-flying robots that perform numerous tasks in a zero-gravity, drag-free, two-dimensional environment. It is envisioned that future generations of these robots will be part of a human-robot team, in which the robots will operate under the task-level commands of astronauts. To make this possible, the ARL has developed a graphical user interface (GUI) with an intuitive object-level motion-direction capability. Using this interface, the ARL has demonstrated autonomous navigation, intercept and capture of moving and spinning objects, object transport, multiple-robot cooperative manipulation, and simple assemblies from both free-flying and fixed bases. The ARL has also built a number of experimental test beds on which the modelling and control of flexible manipulators has been studied. Early ARL experiments in this arena demonstrated for the first time the capability to control the end-point position of both single-link and multi-link flexible manipulators using end-point sensing. Building on these accomplishments, the ARL has been able to control payloads with unknown dynamics at the end of a flexible manipulator, and to achieve high-performance control of a multi-link flexible manipulator

Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement

[EN] Due to the elasticity of their joints, collaborative robots are seldom used in applications with force control. Besides, the industrial robot controllers are closed and do not allow the user to access the motor torques and other parameters, hindering the possibility of carrying out a customized control. A good alternative to achieve a custom force control is sending the output of the force regulator to the robot controller through motion commands (inner/outer loop control). There are different types of motion commands (e.g., position or velocity). They may be implemented in different ways (Jacobian inverse vs. Jacobian transpose), but this information is usually not available for the user. This article is dedicated to the analysis of the effect of different inner loops and their combination with several external controllers. Two of the most determinant factors found are the type of the inner loop and the stiffness matrix. The theoretical deductions have been experimentally verified on a collaborative robot UR3, allowing us to choose the best behaviour in a polishing operation according to pre-established criteria.The authors are grateful for the financial support of the Spanish Ministry of Economy and European Union, grant DPI2016-81002-R (AEI/FEDER, UE), to the research work here published. Rodrigo Perez-Ubeda is grateful to the Ph.D. Grant CONICYT PFCHA/DOCTORADO BECAS CHILE/2017-72180157.Pérez-Ubeda, R.; Zotovic Stanisic, R.; Gutiérrez, SC. (2020). Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement. Applied Sciences. 10(12):1-24. https://doi.org/10.3390/app10124329S1241012Top Trends Robotics 2020—International Federation of Robotics https://ifr.org/ifr-press-releases/news/top-trends-robotics-2020Gaz, C., Magrini, E., & De Luca, A. (2018). A model-based residual approach for human-robot collaboration during manual polishing operations. 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Choosing Poses for Force and Stiffness Control. IEEE Transactions on Robotics, 33(6), 1483-1490. doi:10.1109/tro.2017.2708087Magrini, E., & De Luca, A. (2016). Hybrid force/velocity control for physical human-robot collaboration tasks. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). doi:10.1109/iros.2016.7759151Ahmad, S. (1993). Constrained motion (force/position) control of flexible joint robots. IEEE Transactions on Systems, Man, and Cybernetics, 23(2), 374-381. doi:10.1109/21.229451Calanca, A., & Fiorini, P. (2018). Understanding Environment-Adaptive Force Control of Series Elastic Actuators. IEEE/ASME Transactions on Mechatronics, 23(1), 413-423. doi:10.1109/tmech.2018.2790350Oh, S., & Kong, K. (2017). High-Precision Robust Force Control of a Series Elastic Actuator. IEEE/ASME Transactions on Mechatronics, 22(1), 71-80. doi:10.1109/tmech.2016.2614503Yin, H., Li, S., & Wang, H. (2016). Sliding mode position/force control for motion synchronization of a flexible-joint manipulator system with time delay. 2016 35th Chinese Control Conference (CCC). doi:10.1109/chicc.2016.7554329Ma, Z., Hong, G.-S., Ang, M. H., Poo, A.-N., & Lin, W. (2018). A Force Control Method with Positive Feedback for Industrial Finishing Applications. 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). doi:10.1109/aim.2018.8452689Huang, L., Ge, S. S., & Lee, T. H. (2006). Position/force control of uncertain constrained flexible joint robots. Mechatronics, 16(2), 111-120. doi:10.1016/j.mechatronics.2005.10.002Chiaverini, S., Siciliano, B., & Villani, L. (1999). A survey of robot interaction control schemes with experimental comparison. IEEE/ASME Transactions on Mechatronics, 4(3), 273-285. doi:10.1109/3516.789685Winkler, A., & Suchy, J. (2016). Explicit and implicit force control of an industrial manipulator — An experimental summary. 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). doi:10.1109/mmar.2016.7575081Neranon, P., & Bicker, R. (2016). Force/position control of a robot manipulator for human-robot interaction. Thermal Science, 20(suppl. 2), 537-548. doi:10.2298/tsci151005036nChen, S., Zhang, T., & Zou, Y. (2017). Fuzzy-Sliding Mode Force Control Research on Robotic Machining. Journal of Robotics, 2017, 1-8. doi:10.1155/2017/8128479Lin, H.-I., & Dubey, V. (2018). Design of an Adaptive Force Controlled Robotic Polishing System Using Adaptive Fuzzy-PID. Advances in Intelligent Systems and Computing, 825-836. doi:10.1007/978-3-030-01370-7_64Perez-Vidal, C., Gracia, L., Sanchez-Caballero, S., Solanes, J. E., Saccon, A., & Tornero, J. (2019). Design of a polishing tool for collaborative robotics using minimum viable product approach. International Journal of Computer Integrated Manufacturing, 32(9), 848-857. doi:10.1080/0951192x.2019.1637026Chen, F., Zhao, H., Li, D., Chen, L., Tan, C., & Ding, H. (2019). Contact force control and vibration suppression in robotic polishing with a smart end effector. Robotics and Computer-Integrated Manufacturing, 57, 391-403. doi:10.1016/j.rcim.2018.12.019Mohammad, A. E. K., Hong, J., & Wang, D. (2018). Design of a force-controlled end-effector with low-inertia effect for robotic polishing using macro-mini robot approach. Robotics and Computer-Integrated Manufacturing, 49, 54-65. doi:10.1016/j.rcim.2017.05.011Xiao, C., Wang, Q., Zhou, X., Xu, Z., Lao, X., & Chen, Y. (2019). Hybrid Force/Position Control Strategy for Electromagnetic based Robotic Polishing Systems. 2019 Chinese Control Conference (CCC). doi:10.23919/chicc.2019.8865183Li, J., Zhang, T., Liu, X., Guan, Y., & Wang, D. (2018). A Survey of Robotic Polishing. 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). doi:10.1109/robio.2018.8664890Zollo, L., Siciliano, B., De Luca, A., Guglielmelli, E., & Dario, P. (2004). Compliance Control for an Anthropomorphic Robot with Elastic Joints: Theory and Experiments. Journal of Dynamic Systems, Measurement, and Control, 127(3), 321-328. doi:10.1115/1.1978911Han, D., Duan, X., Li, M., Cui, T., Ma, A., & Ma, X. (2017). Interaction Control for Manipulator with compliant end-effector based on hybrid position-force control. 2017 IEEE International Conference on Mechatronics and Automation (ICMA). doi:10.1109/icma.2017.8015929Schindlbeck, C., & Haddadin, S. (2015). Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks. 2015 IEEE International Conference on Robotics and Automation (ICRA). doi:10.1109/icra.2015.7139036Zotovic Stanisic, R., & Valera Fernández, Á. (2009). 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The International Journal of Robotics Research, 19(9), 835-847. doi:10.1177/02783640022067201Institute of Robotics and Mechatronics DLR Light Weight Robot III https://www.dlr.de/rm/en/desktopdefault.aspx/tabid-12464/#gallery/2916

Impedence Control for Variable Stiffness Mechanisms with Nonlinear Joint Coupling

The current discussion on physical human robot interaction and the related safety aspects, but also the interest of neuro-scientists to validate their hypotheses on human motor skills with bio-mimetic robots, led to a recent revival of tendondriven robots. In this paper, the modeling of tendon-driven elastic systems with nonlinear couplings is recapitulated. A control law is developed that takes the desired joint position and stiffness as input. Therefore, desired motor positions are determined that are commanded to an impedance controller. We give a physical interpretation of the controller. More importantly, a static decoupling of the joint motion and the stiffness variation is given. The combination of active (controller) and passive (mechanical) stiffness is investigated. The controller stiffness is designed according to the desired overall stiffness. A damping design of the impedance controller is included in these considerations. The controller performance is evaluated in simulation