Controller-observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system

[EN] Rehabilitation is a hazardous task for a mechanical system, since the device has to interact with the human extremities without the hands-on experience the physiotherapist acquires over time. A gap needs to be filled in terms of designing effective controllers for this type of devices. In this respect, the paper describes the design of a model-based control for an electromechanical lower-limb rehabilitation system based on a parallel kinematic mechanism. A controller-observer was designed for estimating joint velocities, which are then used in a hybrid position/force control scheme. The model parameters are identified by customising an approach based on identifying only the relevant system dynamics parameters. Findings obtained through simulations show evidence of improvement in tracking performance compared with those where the velocity was estimated by numerical differentiation. The controller is also implemented in an actual electromechanical system for lower-limb rehabilitation tasks. Findings based on rehabilitation tasks confirm the findings from simulations.This work was partially financed by the Plan Nacional de I+D, Comision Interministerial de Ciencia y Tecnologia (FEDERCICYT) under the project DPI2013-44227-R and by the Instituto U. de Automatica e Informatica Industrial (ai2) of the Universitat Politecnica de Valencia.Valera Fernández, Á.; Díaz-Rodríguez, M.; Vallés Miquel, M.; Oliver, E.; Mata Amela, V.; Page Del Pozo, AF. (2017). Controller-observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system. International Journal of Control. 90(4):702-714. https://doi.org/10.1080/00207179.2016.1215529S702714904Åström, K. J., & Murray, R. M. (2010). Feedback Systems. doi:10.2307/j.ctvcm4gdkAtkeson, C. G., An, C. H., & Hollerbach, J. M. (1986). Estimation of Inertial Parameters of Manipulator Loads and Links. The International Journal of Robotics Research, 5(3), 101-119. doi:10.1177/027836498600500306Chia Bejarano, N., Maggioni, S., De Rijcke, L., Cifuentes, C. A., & Reinkensmeyer, D. J. (2015). Robot-Assisted Rehabilitation Therapy: Recovery Mechanisms and Their Implications for Machine Design. Emerging Therapies in Neurorehabilitation II, 197-223. doi:10.1007/978-3-319-24901-8_8Berghuis, H., & Nijmeijer, H. (1993). A passivity approach to controller-observer design for robots. IEEE Transactions on Robotics and Automation, 9(6), 740-754. doi:10.1109/70.265918Briot, S., & Gautier, M. (2013). Global identification of joint drive gains and dynamic parameters of parallel robots. Multibody System Dynamics, 33(1), 3-26. doi:10.1007/s11044-013-9403-6Canudas de Wit, C., & Fixot, N. (1991). Robot control via robust estimated state feedback. IEEE Transactions on Automatic Control, 36(12), 1497-1501. doi:10.1109/9.106170Canudas de Wit, C., & Slotine, J.-J. E. (1991). Sliding observers for robot manipulators. Automatica, 27(5), 859-864. doi:10.1016/0005-1098(91)90041-yCao, J., Xie, S. Q., Das, R., & Zhu, G. L. (2014). Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects. Medical Engineering & Physics, 36(12), 1555-1566. doi:10.1016/j.medengphy.2014.08.005Carretero, J. A., Podhorodeski, R. P., Nahon, M. A., & Gosselin, C. M. (1999). Kinematic Analysis and Optimization of a New Three Degree-of-Freedom Spatial Parallel Manipulator. Journal of Mechanical Design, 122(1), 17-24. doi:10.1115/1.533542Cazalilla, J., Vallés, M., Mata, V., Díaz-Rodríguez, M., & Valera, A. (2014). Adaptive control of a 3-DOF parallel manipulator considering payload handling and relevant parameter models. Robotics and Computer-Integrated Manufacturing, 30(5), 468-477. doi:10.1016/j.rcim.2014.02.003De Jalon, J.G. & Bayo, E. (1994). Kinematic and dynamic simulation of multibody systems: the real-time challenge. New York: Springer Verlag.Díaz, I., Gil, J. J., & Sánchez, E. (2011). Lower-Limb Robotic Rehabilitation: Literature Review and Challenges. Journal of Robotics, 2011, 1-11. doi:10.1155/2011/759764Díaz-Rodríguez, M., Iriarte, X., Mata, V., & Ros, J. (2009). On the Experiment Design for Direct Dynamic Parameter Identification of Parallel Robots. Advanced Robotics, 23(3), 329-348. doi:10.1163/156855308x397550Díaz-Rodríguez, M., Mata, V., Valera, Á., & Page, Á. (2010). A methodology for dynamic parameters identification of 3-DOF parallel robots in terms of relevant parameters. Mechanism and Machine Theory, 45(9), 1337-1356. doi:10.1016/j.mechmachtheory.2010.04.007Gautier, M. (1991). Numerical calculation of the base inertial parameters of robots. Journal of Robotic Systems, 8(4), 485-506. doi:10.1002/rob.4620080405Gautier, M., & Khalil, W. (s. f.). On the identification of the inertial parameters of robots. Proceedings of the 27th IEEE Conference on Decision and Control. doi:10.1109/cdc.1988.194738Jamwal, P. K., Hussain, S., & Xie, S. Q. (2013). Review on design and control aspects of ankle rehabilitation robots. Disability and Rehabilitation: Assistive Technology, 10(2), 93-101. doi:10.3109/17483107.2013.866986Janabi-Sharifi, F., Hayward, V., & Chen, C.-S. J. (2000). Discrete-time adaptive windowing for velocity estimation. IEEE Transactions on Control Systems Technology, 8(6), 1003-1009. doi:10.1109/87.880606Janot, A., Gautier, M., Jubien, A., & Vandanjon, P. O. (2014). Comparison Between the CLOE Method and the DIDIM Method for Robots Identification. IEEE Transactions on Control Systems Technology, 22(5), 1935-1941. doi:10.1109/tcst.2014.2299544Janot, A., Vandanjon, P.-O., & Gautier, M. (2016). A revised Durbin-Wu-Hausman test for industrial robot identification. Control Engineering Practice, 48, 52-62. doi:10.1016/j.conengprac.2015.12.017Jiménez-Fabián, R., & Verlinden, O. (2012). Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Medical Engineering & Physics, 34(4), 397-408. doi:10.1016/j.medengphy.2011.11.018Khalil, W., Vijayalingam, A., Khomutenko, B., Mukhanov, I., Lemoine, P., & Ecorchard, G. (2014). OpenSYMORO: An open-source software package for symbolic modelling of robots. 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. doi:10.1109/aim.2014.6878246Marchal-Crespo, L., & Reinkensmeyer, D. J. (2009). Review of control strategies for robotic movement training after neurologic injury. Journal of NeuroEngineering and Rehabilitation, 6(1). doi:10.1186/1743-0003-6-20Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., & Xie, S. (Shane). (2015). Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics, 31, 132-145. doi:10.1016/j.mechatronics.2015.04.005Page, A., Candelas, P., & Belmar, F. (2006). On the use of local fitting techniques for the analysis of physical dynamic systems. European Journal of Physics, 27(2), 273-279. doi:10.1088/0143-0807/27/2/010Raibert, M. H., & Craig, J. J. (1981). Hybrid Position/Force Control of Manipulators. Journal of Dynamic Systems, Measurement, and Control, 103(2), 126-133. doi:10.1115/1.3139652Ramsay, J. O., & Silverman, B. W. (2005). Functional Data Analysis. Springer Series in Statistics. doi:10.1007/b98888Saglia, J. A., Tsagarakis, N. G., Dai, J. S., & Caldwell, D. G. (2013). Control Strategies for Patient-Assisted Training Using the Ankle Rehabilitation Robot (ARBOT). IEEE/ASME Transactions on Mechatronics, 18(6), 1799-1808. doi:10.1109/tmech.2012.2214228Vallés, M., Cazalilla, J., Valera, Á., Mata, V., Page, Á., & Díaz-Rodríguez, M. (2015). A 3-PRS parallel manipulator for ankle rehabilitation: towards a low-cost robotic rehabilitation. Robotica, 35(10), 1939-1957. doi:10.1017/s0263574715000120Vallés, M., Cazalilla, J. I., Valera, Á., Mata, V., & Page, Á. (2013). Implementación basada en el middleware OROCOS de controladores dinámicos pasivos para un robot paralelo. Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(1), 96-103. doi:10.1016/j.riai.2012.11.009Vallés, M., Díaz-Rodríguez, M., Valera, Á., Mata, V., & Page, Á. (2012). Mechatronic Development and Dynamic Control of a 3-DOF Parallel Manipulator. Mechanics Based Design of Structures and Machines, 40(4), 434-452. doi:10.1080/15397734.2012.687292Wu, F. X., Zhang, W. J., Li, Q., & Ouyang, P. R. (2002). Integrated Design and PD Control of High-Speed Closed-loop Mechanisms. Journal of Dynamic Systems, Measurement, and Control, 124(4), 522-528. doi:10.1115/1.1513179Yang, C., Huang, Q., & Han, J. (2012). Decoupling control for spatial six-degree-of-freedom electro-hydraulic parallel robot. Robotics and Computer-Integrated Manufacturing, 28(1), 14-23. doi:10.1016/j.rcim.2011.06.002Yoon, J., Ryu, J., & Lim, K.-B. (2006). Reconfigurable ankle rehabilitation robot for various exercises. Journal of Robotic Systems, 22(S1), S15-S33. doi:10.1002/rob.2015

A Next-Generation Liquid Xenon Observatory for Dark Matter and Neutrino Physics

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for Weakly Interacting Massive Particles (WIMPs), while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector