Upper-limb Rehabilitation Robot Motion Control Based on Dynamic Interpolation
PAN Lizheng1, SONG Aiguo1, XU Guozheng2, LI Huijun1, XU Baoguo1
1. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;
2. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
A control system design method based on dynamic interpolation strategy is proposed to improve the motion control performance of rehabilitation robot. Firstly, the impaired-limb movement features are extracted with sliding standard deviation for a certain data samples, and the real-time physical state of impaired limb is acquired by fuzzy reasoning. Secondly, the appropriate interpolation method for motion control is recommended using dynamic interpolation decision-making mechanism. Finally, the position-based impedance control is adopted to carry out training exercise. The proposed method avoids the limitations of the conventional single-interpolation methods, and effectively integrates the characteristics of different interpolation methods. Experimental results based on WAMTM rehabilitation robot show that the designed control system with proposed method demonstrates better motion control performances under disturbances.
[1] Laura M C, David J R. Review of control strategies for robotic movement training after neurologic injury[J]. Journal of NeuroEngineering and Rehabilitation, 2009, 6: 20.
[2] Choi Y, Gordon J, Kim D. An adaptive automated robotic task-practice system for rehabilitation of arm functions after stroke[J]. IEEE Transactions on Robotics, 2009, 25(3): 556-568.
[3] Adel O, Richard B, Olivier G, et al. Feedrate planning for machining with industrial six-axis robots[J]. Control Engineering Practice, 2010, 18(5): 471-482.
[4] Wang C, Xu S. Lagrange fuzzy interpolating controlling and simulation on semi-active suspension of vehicle[C]//Inter-national Conference on Intelligent Computation Technology and Automation. Piscataway, NJ, USA: IEEE, 2009: 78-81.
[5] Bai Y, Wang D L. On the comparison of trilinear, cubic spline, and fuzzy interpolation methods in the high-accuracy measurements[J]. IEEE Transactions on Fuzzy Systems, 2010, 18(5): 1016-1022.
[6] Guan Y S, Yokoi K, Stasse O, et al. On robotic trajectory planning using polynomial interpolations[C]//IEEE International Conference on Robotics and Biomimetics. Piscataway, NJ, USA: IEEE, 2005: 111-116.
[7] Tian L F, Collins C. An effective robot trajectory planning method using a genetic algorithm[J]. Mechatronics, 2004, 14(5): 455-470.
[8] Li T H, Su Y T, Lai S W, et al. Walking motion generation, synthesis, and control for biped robot by using PGRL, LPI, and fuzzy logic[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, 41(3): 736-748.
[9] Yi S Y. Reliable gait planning and control for miniaturized quadruped robot pet[J]. Mechatronics, 2010, 20(4): 485-495.
[10] Harada K, Hattori S, Hirukawa H, et al. Two-stage time-parameterized gait planning for humanoid robots[J]. IEEE/ASME Transactions on Mechatronics, 2010, 15(5): 694-703.
[11] 杨俊友,白殿春,王硕玉,等.全方位轮式下肢康复训练机器人轨迹跟踪控制[J]. 机器人,2011,33(3): 314-318. Yang J Y, Bai D C, Wang S Y, et al. Trajectory tracking control of omni-directional wheeled robot for lower limbs rehabilitative training[J]. Robot, 2011, 33(3): 314-318.
[12] Choi J. Approximate MAP detection with ordering and successive processing for iterative detection and decoding in MIMO systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(8): 1415-1425.
[13] Song A G, Wu J, Qin G, et al. A novel self-decoupled four degree-of-freedom wrist force/torque sensor[J]. Measurement, 2007, 40(9/10): 883-891.
[14] 徐国政,宋爱国,李会军.基于模糊逻辑的上肢康复机器人阻抗控制实验研究[J].机器人,2010,32(6):792-798. Xu G Z, Song A G, Li H J. Experimental study on fuzzy-logic-based impedance control for upper-limb rehabilitation robot[J]. Robot, 2010, 32(6): 792-798.
[15] 蔡自兴.机器人学[M].第二版.北京:清华大学出版社,2009. Cai Z X. Robotics[M]. 2nd ed. Tsinghua University Press, 2009.
[16] Gustavo S, Andres R, Lucia Q, et al. A linear-interpolation-based controller design for trajectory tracking of mobile robots[J]. Control Engineering Practice, 2010, 18(3): 318-329.
[17] Hogan N. Impedance control: An approach to manipulation: Part 1 - Theory, Part 2 - Implementation, and Part 3 - Application[J]. Journal of Dynamic Systems, Measurement, and Control, 1985, 107(1): 1-24.
[18] Qiao B, Lu R J. Impedance force control for position controlled robotic manipulators under the constraint of unknown environments[J]. Journal of Southeast University, 2003, 19(4): 359-363.
