| 研究生: |
黃昭凱 Huang, Chao-Kai |
|---|---|
| 論文名稱: |
應用具有不等式拘束的微分動態程序於最佳復健路徑之規劃 Applying differential dynamic program with inequality constraints for optimal rehabilitation trajectories planning |
| 指導教授: |
田思齊
Tien, Szu-Chi |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 論文出版年: | 2019 |
| 畢業學年度: | 107 |
| 語文別: | 中文 |
| 論文頁數: | 61 |
| 中文關鍵詞: | 有拘束之最佳化控制 、微分動態程序 、復健路徑規劃 |
| 外文關鍵詞: | optimal control with constraints, differential dynamic program, rehabilitation trajectories planning |
| 相關次數: | 點閱:119 下載:6 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本研究以上肢復健為例,建立一個基於微分動態程序之復健最佳路徑的規劃方法。此方法考慮患者關節所受扭矩的極限,以及肢體運動的拘束。在參考復健師規劃之標準路徑的情況下,經由微分動態程序,計算出最適合患者之上肢復健運動路徑及最佳關節扭矩。在復健師規劃路徑方面,為使復健師方便建立參考之復健路徑,我們藉由一多項式來模擬標準路徑。在扭矩拘束方面,藉由調整權重及設定上下限來使關節扭矩可以被拘束在一固定範圍內。至於肢體運動的拘束,將其表現在成本函數中,期望微分動態程序在使成本值變小的過程中,肢體運動路徑能逐漸滿足拘束。此外,我們經由模擬的方式建立復健機馬達驅動的輔助力,除了幫助患者達到最佳復健路徑外,也可藉由調整輔助程度來增加或減少復健強度。模擬結果顯示,成本值在疊代過程中越變越小,且增加成本函數中的追蹤誤差項權重時,最佳復健路徑的確和標準路徑相像。最佳關節扭矩滿足拘束,雖部分之肢體運動路徑超出拘束,但不違反人體正常運動姿態。
In this study, a method based on differential dynamic program is proposed for planning optimal rehabilitation trajectories and elaborated in an upper limb rehabilitation example. This method considers the constraints of both patients' joint torques and limb movements. Taking the standard path planned by a physical therapist as reference, an optimal upper limb rehabilitation trajectory with optimal joint torques can be calculated by using differential dynamic program. In terms of planning the standard path, a rehabilitation path described by a polynomial was used to simplify the path-setting process for physical therapists. In terms of torque constraints, they can be con fined within a predefined upper and lower bounds by adjusting the torque-weighting in the cost function. As for the constraints of limb movements, they were considered and expressed in the cost function, expecting that they could satisfy the constraints when cost value went down. In addition, the auxiliary force driven by the motor of the rehabilitation machine was set to be adjustable in simulation; therefore, this hypothetic system can not only help the patients to achieve the optimal rehabilitation trajectories, but also increase or decrease the assistant strength. Simulation results show that, (1) the cost value becomes smaller and smaller in the iterative process, (2) the optimal rehabilitation trajectory is similar to the reference path when the weighting of tracking errors in the cost function increases, (3) the optimal joint torque satisfies the constraints. Although part of the limb movement exceeds the predefined constraints, it does not violate the normal posture of human beings.
[1] World Population Prospects 2019: Highlights. United National, 2019.
[2] 復健醫學講義. 台中榮總復健科.
[3] C. Carignan et al. Design of an arm exoskeleton with scapula motion for shoulder rehabilitation: Book design of an arm exoskeleton with scapula motion for shoulder rehabilitation. pages 524-531, 2005.
[4] Y. Bouteraa and I. Ben Abdallah. Exoskeleton robots for upper-limb rehabilitation. 2016 13th International Multi-Conference on Systems, Signals Devices(SSD), 16:1-6, 2016.
[5] L.Lin et al. A novel multi-dof exoskeleton robot for upper limb rehabilitation:a single-blinded randomized trial in two centers. Stroke 2005, 36, 2016.
[6] S. K. Banala et al. Active leg exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients. 2007 IEEE 10th International Conference on Rehabilitation Robotics, pages 401-407, 2007.
[7] S. K. Banala et al. Robot assisted gait training with active leg exoskeleton(ALEX). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(1):2-8, 2009.
[8] S.Hesse et al. Computerized arm training improves the motor control of the severely affected arm after stroke. Journal of Mechanics in Medicine and Biology., 16(9), 2005.
[9] G.Fazekas et al. Robot-mediated upper limb physiotherapy for patients with spastic hemiparesis: A preliminary study. Journal of Rehabilitation Medicine., 39(7):580-582, 2007.
[10] J.Mehrholz et al. Electromechanical and robot-assisted arm training for improving arm function and activities of daily living after stroke. Cochrane Database of Systematic Reviews, (4), 2008.
[11] Z. Xie et al. Differential dynamic programming with nonlinear constraints. 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 695-702, 2017.
[12] Y. Tassa et al. Control-limited differential dynamic programming. 2014 IEEE International Conference on Robotics and Automation (ICRA), pages 1168-1175, 2014.
[13] C. Liu and J. Su. Implementation of a trajectory library approach to controlling humanoid standing balance. 2011 IEEE International Conference on Systems, Man, and Cybernetics, pages 1502-1507, 2011.
[14] S. Kajita et al. Quick squatting motion generation of a humanoid robot for falling damage reduction. 2017 IEEE International Conference on Cyborg and Bionic Systems (CBS), pages 45-49, 2017.
[15] R. Budhiraja et al. Differential dynamic programming for multi-phase rigid contact dynamics. 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pages 1-9, 2018.
[16] G. Milad et al. Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance. Autonomous Robots, 41(3):575-592,2017.
[17] J. Shi et al. A method for constrained dynamic optimization problems. 1990 American Control Conference, pages 830-835, 1990.
[18] D. Murray and S. Yakowitz. Constrained differential dynamic programming and its application to multireservoir control. Water Resources Research,15(5):1017-1027, 1979.
[19] G. Marco et al. A robotic system to train activities of daily living in a virtual environment. Medical & Biological Engineering & Computing, 49(10):1213-1223, 2011.
[20] T. Nef et al. ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation. Applied Bion Biomech, 6(2):127-142, 2009.
[21] M. Bergamasco. An exoskeleton structure for physical interaction with a human being. PCT Application N. WO2013186701 (A1), 2013.
[22] E. Pirondini et al. Evaluation of the e ects of the arm light exoskeleton on movement execution and muscle activities: a pilot study on healthy subjects. NeuroEngineering and Rehabilitation, 2016.
[23] A. Zeiaee et al. Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients. 2017 International Conference on Rehabilitation Robotics (ICORR), pages 759-764, 2017.
[24] D. Paolo. Adjustments to zatsiorsky-seluyanov's segment inertia parameters. Journal of biomechanics, 29(9):1223-1230, 1996.
[25] R. Jazar. Advanced Dynamics: Rigid Body, Multibody, and Aerospace Applications. John Wiley & Sons, inc, 2011.
[26] L.Z. Liao and C.A. Shoemaker. Convergence in unconstrained discrete-time differential dynamic programming. IEEE Transactions on Automatic Control, 36(6):692-706, 1991.
[27] D. Mayne. A second-order gradient method for determining optimal trajectories of nonlinear discrete-time systems. International Journal Of Control, 3:85-95, 1966.
[28] Y. Tassa. Theory and Implementation of Biomimetic Motor Controllers. PhD thesis, Hebrew University of Jerusalem, 2011.