Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T17:19:18.291Z Has data issue: false hasContentIssue false
  • English
  • Français

Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

Published online by Cambridge University Press:  17 July 2020

Andres Rodriguez Reina ,
Kim-Doang Nguyen Open the ORCID record for Kim-Doang Nguyen [Opens in a new window]  and
Harry Dankowicz
Andres Rodriguez Reina
Affiliation:
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. E-mails: [email protected], [email protected]
Kim-Doang Nguyen*
Affiliation:
Department of Mechanical Engineering, South Dakota State University, Brookings, SD, USA
Harry Dankowicz
Affiliation:
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper reports on laboratory and field experimental results for controlled robotic manipulators operating on moving platforms with unmodeled dynamics. The aim is to validate theoretical predictions for the dependence on control parameters of an adaptive control strategy. In addition, the results provide insight into different discretizations of the continuous-time formulation, suggesting the most suitable discretization scheme for hardware implementation. The second set of experimental results, obtained from an implementation of the control framework for synchronization and consensus in networks of robotic manipulators, similarly validate theoretical predictions on the sensitivity to network communication delays.

Keywords

Adaptive controlNetworked robotsExperimental validationUnmodeled dynamicsDynamic platforms
Type
Articles
Information
Robotica , Volume 39 , Issue 4 , April 2021 , pp. 582 - 605
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bergerman, M., van Henten, E., Billingsley, J., Reid, J. and Mingcong, D., “IEEE robotics and automation society technical committee on agricultural robotics and automation,” IEEE Robot. Autom. Mag. 20(2), 20–23 (2013).10.1109/MRA.2013.2255513CrossRefGoogle Scholar
van Henten, E., Slot, D. V., Hol, C. and Willigenburg, L. V., “Optimal manipulator design for a cucumber harvesting robot,” Comput. Electron. Agric. 65(2), 247–257 (2009).CrossRefGoogle Scholar
van Henten, E., Schenk, E., van Willigenburg, L., Meuleman, J. and Barreiro, P., “Collision-free inverse kinematics of the redundant seven-link manipulator used in a cucumber picking robot,” Biosyst. Eng. 106(2), 112–124 (2010).CrossRefGoogle Scholar
Foglia, M. M. and Reina, G., “Agricultural robot for radicchio harvesting,” J. Field Robot. 23(6–7), 363–377 (2006).CrossRefGoogle Scholar
Edan, Y., Rogozin, D., Flash, T. and Miles, G., “Robotic melon harvesting,” IEEE Trans. Robot. Autom. 16(6), 831–835 (2000).CrossRefGoogle Scholar
Johnson, D. A., Naffin, D. J., Puhalla, J. S., Sanchez, J. and Wellington, C. K., “Development and implementation of a team of robotic tractors for autonomous peat moss harvesting,” J. Field Robot. 26(6–7), 549–571 (2009).CrossRefGoogle Scholar
Das, J., Cross, G., Qu, C., Makineni, A., Tokekar, P., Mulgaonkar, Y. and Kumar, V., “Devices, Systems, and Methods for Automated Monitoring Enabling Precision Agriculture,” Proceedings of the IEEE International Conference on Automation Science and Engineering (2015) pp. 462–469.Google Scholar
Slaughter, D., Giles, D. and Downey, D., “Autonomous robotic weed control systems: A review,” Comput. Electron. Agric. 61(1), 63–78 (2008).CrossRefGoogle Scholar
Kacira, M., Ling, P. P. and Short, T. H., “Machine vision extracted plant movement for early detection of plant water stress,” Trans. Am. Soc. Agric. Eng. 45(4), 1147–1153 (2002).CrossRefGoogle ScholarPubMed
Story, D., Kacira, M., Kubota, C., Akoglu, A. and An, L., “Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments,” Comput. Electron. Agric. 74(2), 238–243 (2010).CrossRefGoogle Scholar
Gravalos, I. G., Moshou, D. E., Loutridis, S. J., Gialamas, T. A., Kateris, D. L., Tsiropoulos, Z. T. and Xyradakis, P. I., “Design of a pipeline sensor-based platform for soil water content monitoring,” Biosyst. Eng. 113(1), 1–10 (2012).10.1016/j.biosystemseng.2012.06.001CrossRefGoogle Scholar
Hernandez, J. D., Barrientos, J., del Cerro, J., Barrientos, A. and Sanz, D., “Moisture measurement in crops using spherical robots,” Ind. Robot Int. J. 40(1), 59–66 (2013).CrossRefGoogle Scholar
Lee, W. S., Slaughter, D. C. and Giles, D. K., “Robotic weed control system for tomatoes,” Precis. Agric. 1(1), 95–113 (1999).CrossRefGoogle Scholar
Lamm, R., Slaughter, D. and Giles, D., “Precision weed control system for cotton,” Trans. Am. Soc. Agric. Eng. 45(1), 231–238 (2002).Google Scholar
Berenstein, R., Shahar, O. B. and Edan, A. S. Y., “Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer,” Intell. Serv. Robot. 3(4), 233–243 (2010).CrossRefGoogle Scholar
Morales, J., Martinez, J. L., Mandow, A., Seron, J. and Garcia-Cerezo, A. J., “Static tip-over stability analysis for a robotic vehicle with a single-axle trailer on slopes based on altered supporting polygons,” IEEE/ASME Trans. Mechatron. 18(2), 697–705 (2013).CrossRefGoogle Scholar
Dubowsky, S. and Papadopoulos, E., “The kinematics, dynamics, and control of free-flying and free-floating space robotic systems,” IEEE Trans. Robot. Autom. 9(5), 531–543 (1993).CrossRefGoogle Scholar
Parlaktuna, O. and Ozkan, M., “Adaptive control of free-floating space manipulators using dynamically equivalent manipulator model,” Robot. Auton. Syst. 46(3), 185–193 (2004).CrossRefGoogle Scholar
Wang, H. and Xie, Y., “Passivity based adaptive Jacobian tracking for free-floating space manipulators without using spacecraft acceleration,” Automatica 45(6), 1510–1517 (2009).CrossRefGoogle Scholar
Abiko, S. and Yoshida, K., “Adaptive reaction control for space robotic applications with dynamic model uncertainty,” Adv. Robot. 24(8), 1099–1126 (2010).CrossRefGoogle Scholar
Boukattaya, M., Jallouli, M. and Damak, T., “On trajectory tracking control for nonholonomic mobile manipulators with dynamic uncertainties and external torque disturbances,” Robot. Auto. Syst. 60, 1640–1647 (2012).CrossRefGoogle Scholar
Andaluz, V., Carelli, R., Salinas, L., Toibero, J. M. and Roberti, F., “Visual control with adaptive dynamical compensation for 3d target tracking by mobile manipulators,” Mechatronics 22(4), 499–502 (2012).CrossRefGoogle Scholar
Ismail, Z. and Dunnigan, M., “Tracking control scheme for an underwater vehicle-manipulator system with single and multiple sub-regions and sub-task objectives,” IET Control Theory Appl. 5(5), 721–735 (2011).CrossRefGoogle Scholar
Yokokohji, Y., Toyoshima, T. and Yoshikawa, T., “Efficient computational algorithms for trajectory control of free-flying space robots with multiple arms,” IEEE Trans. Robot. Autom. 9(5), 571–580 (1993).CrossRefGoogle Scholar
Wang, H., “On the recursive adaptive control for free-floating space manipulators,” J. Intell. Robot. Syst. 66(4), 443–461 (2012).CrossRefGoogle Scholar
Chu, Z., Sun, F. and Cui, J., “Fuzzy adaptive disturbance-observer-based robust tracking control of electrically driven free-floating space manipulator,” IEEE Syst. J. 8(2), 343–352 (2014).CrossRefGoogle Scholar
Antonelli, G., Underwater Robots: Control of Vehicle Manipulator Systems (Springer-Verlag, New York, 2006).Google Scholar
Lewis, F., Jagannathan, S. and Yesildirek, A., Neural Network Control of Robot Manipulators and Nonlinear Systems (Taylor & Francis, London, 1999).Google Scholar
Hovakimyan, N. and Cao, C., L1 Adaptive Control Theory: Guaranteed Robustness with Fast Adaptation (SIAM, Philadelphia, PA, USA, 2010).CrossRefGoogle Scholar
Nguyen, K.-D., Dankowicz, H. and Hovakimyan, N., “Marginal Stability in l1-Adaptive Control of Manipulators,” ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2013) pp. V07AT10A055.Google Scholar
Nguyen, K.-D. and Dankowicz, H., “Adaptive control of underactuated robots with unmodeled dynamics,” Robot. Auto. Syst. 64, 84–99 (2015).CrossRefGoogle Scholar
Nguyen, K.-D. and Dankowicz, H., “Synchronization and Consensus of a Robot Network on an Underactuated Dynamic Platform,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (2014) pp. 117–122.Google Scholar
Nguyen, K.-D. and Dankowicz, H., “Cooperative control of networked robots on a dynamic platform in the presence of communication delays,” Int. J. Robust Nonlinear Control 27(9), 1433–1461 (2017).Google Scholar
Dobrokhodov, V., Kaminer, I., Kitsios, I., Xargay, E., Cao, C., Gregory, I. M., Hovakimyan, N. and Valavani, L., “Experimental validation of L1 adaptive control: The Rohrs counterexample in flight,” J. Guidance Control Dyn. 34(5), 1311–1328 (2011).CrossRefGoogle Scholar
Hovakimyan, N., Cao, C., Kharisov, E., Xargay, E. and Gregory, I. M., “L1 adaptive control for safety-critical systems,” IEEE Control Syst. 31(5), 54–104 (2011).Google Scholar
Xargay, E., Dobrokhodov, V., Kaminer, I., Pascoal, A. M., Hovakimyan, N. and Cao, C., “Time-critical cooperative control of multiple autonomous vehicles: Robust distributed strategies for path-following control and time coordination over dynamic communications networks,” IEEE Control Syst. 32(5), 49–73 (2012).Google Scholar
Ackerman, K. A., Xargay, E., Choe, R., Hovakimyan, N., Cotting, M. C., Jeffrey, R. B., Blackstun, M. P., Fulkerson, T. P., Lau, T. R. and Stephens, S. S., “Evaluation of an L1 adaptive flight control law on Calspan’s variable-stability Learjet,” J. Guidance Control Dyn. 40(4), 1051–1060 (2017).CrossRefGoogle Scholar
Nguyen, K.-D., Rodriguez Reina, A. and Dankowicz, H., “End-effector control for manipulators operating on dynamic platforms,” (unpublished, 2016).Google Scholar
Cao, C. and Hovakimyan, N., “L1 adaptive output-feedback controller for non-strictly-positive-real reference systems: missile longitudinal autopilot design,” AAIA J. Guidance Control Dyn. 32(3), 717–726 (2009).CrossRefGoogle Scholar
Nagle, J., “Congestion control in IP/TCP internetworks,” ACM SIGCOMM Comput. Commun. Rev. 25(1), 61–65 (1995).CrossRefGoogle Scholar
Minshall, G., Saito, Y., Mogul, J. C. and Verghese, B., “Application performance pitfalls and TCP’s nagle algorithm,” ACM Sigmetrics Perform. Eval. Rev. 27(4), 36–44 (2000).CrossRefGoogle Scholar
Mahony, R., Hamel, T. and Pflimlin, J.-M., “Nonlinear complementary filters on the special orthogonal group,” IEEE Trans. Autom. Control 53(5), 1203–1218 (2008).CrossRefGoogle Scholar
Nguyen, K.-D., Li, Y. and Dankowicz, H., “Delay robustness of an L1 adaptive controller for a class of systems with unknown matched nonlinearities,” IEEE Trans. Autom. Control 62(10), 5485–5491 (2017).CrossRefGoogle Scholar
Bogacki, P. and Shampine, L. F., “A 3(2) pair of Runge–Kutta formulas,” Appl. Math. Lett. 2(4), 321–325 (1989).CrossRefGoogle Scholar
Li, Y., Nguyen, K.-D. and Dankowicz, H., “A Robust Adaptive Controller for a Seed Refilling System on a Moving Platform,” Proceedings of 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture, IFAC-PapersOnLine 49(16), 341–346 (2016).Google Scholar
Rodriguez Reina, A., Experimental Validation of an L1 Controller on a Single Robotic Manipulator on a Moving Platform and a Robotic Cooperative Network MS Thesis (University of Illinois at Urbana–Champaign, 2017).Google Scholar
Stroupe, A., Huntsberger, T., Okon, A., Aghazarian, H. and Robinson, M., “Behavior-Based Multi-robot Collaboration for Autonomous Construction Tasks,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (2005) pp. 1495–1500.Google Scholar