被動與主動運動之單自由度下肢復健系統

本論文製作出一套專為需要進行下肢復健訓練的患者所使用的單自由度下肢復健系統，並針對復健醫學中的運動治療法設計了兩個復健訓練模式：(1)被動運動模式，(2)主動運動模式。被動與主動運動之單自由度下肢復健系統的復健訓練運動主要有三大目的：一、維持或增加髖關節及膝關節的活動度；二、增加下肢肌力與耐力，尤其是股四頭肌及大腿前面的肌肉力量，且最後伸直角度的力量是訓練的重點；三、協調能力與平衡能力的訓練。首先，針對我們所設計出的單自由度下肢復健系統，利用虛功原理推導出系統的動態模型。復健系統為一種人機合作的系統，不同患者的下肢參數會造成系統的不確定性，因此克服系統參數的變動是首要解決的課題。利用順滑模態控制系統強健性的特性，設計控制器使復健機在外擾及系統不確定性下，可以準確地追蹤到期望的角位置及角速度軌跡。接著，由於傳統的順滑模態控制系統會因為不連續控制力的影響，產生切跳現象，為了削弱切跳現象，我們提出一智慧型順滑模態控制系統。此一智慧型順滑模態控制系統是利用遞迴式柴比雪夫類神經網路估測器去即時估測未知的系統外擾及不確定性項。經由外擾估測器的輸出，設計一類神經網路的控制器，並為了增加系統的強健性，另外又設計一強健控制器。然後利用里奧波諾夫理論證明我們所提出的智慧型順滑模態控制系統為漸進穩定。利用前面所設計的智慧型順滑模態控制器去完成被動運動模式的軌跡控制，而為了增加復健系統的安全性及舒適性，修改原本的被動運動模式控制架構，提出了改良式被動運動模式，在原本的控制架構加上了基於位置的阻抗控制架構，利用量測人機之間的相互作用力，若力量感測器量測到異常大的訊號，復健機隨時可以做出軌跡的調整以保護病人造成訓練傷害。並且再利用此架構去設計、完成主動運動模式，透過適當的調整阻抗控制器參數，調整主動運動的肌力訓練強度。最後，經由模擬與實驗結果驗證，此復健系統於被動運動模式時，即便承載下肢時也可以順利的追蹤軌跡命令，並比較控制器證明本論文提出的智慧型順滑模態控制器的追蹤能力及強健性是相對最優越；而操作在主動運動模式時，也可以透過調整阻抗控制器參數，適當的加強肌力訓練的強度。

關鍵字

智慧型順滑模態控制器；類神經網路估測器；阻抗控制

並列摘要

In this study, we present the design and control of single DOF lower limb rehabilitation system for a patient who needs lower limb rehabilitation. Based on physical therapy, we designed two rehabilitation mode for different rehabilitation purposes: (1)Passive-motion mode (2) Active-motion mode. Passive and active motion of single DOF lower limb rehabilitation system has three purposes: (1) to increase the hip and knee range of motion (2) to increase the quadriceps muscle strength and endurance (3) training of the Coordination and balance ability. First, the dynamic model of the rehabilitation machine is derived by the principal of virtual work in dynamics. The major control problem of a rehabilitation machine is to overcome the system parameter variation, since each patient has the different body segment parameters. Sliding-mode control (SMC) is a comprehensive power control scheme which has been successfully used in both linear and nonlinear systems. A major advantage of SMC is their stabilizing properties for dynamic system subject to disturbance and uncertainties. In contrast, the chattering phenomenon is caused by discontinuous hitting control effort. For reducing the chattering phenomenon, we proposed an intelligent sliding-mode control (ISMC) system which involved recurrent Chebyshev neural network (RCNN) estimator to estimate the unknown external disturbance and uncertainty online is proposed to track the angular position and velocity of the rehabilitation machine. Furthermore, we proved that the proposed ISMC system is asymptotically stable via Lyapunov theory. Passive-motion mode of the rehabilitation system is achieved by the ISMC system. For increasing safety and comfort of passive-motion mode, we added position-based impedance control architecture in the ISMC system. If the force sensor which is located at the end-effector of the rehabilitation machine measures an unusual interaction force between patient and machine, the rehabilitation machine can adjust the trajectories immediately for protecting the patient. Moreover, we designed active-motion mode by utilizing this control architecture. It can modulate the intensity in the strength training of active-motion mode through adjusting the parameters in the impedance controller. Finally, the simulation and experiment result proved that the ISMC system has superior load-carrying capacity and tracking ability when it operated in the passive-motion mode. When the rehabilitation system operated in active-motion mode, it can increase the intensity in the strength training by adjusting the parameters in the impedance controller appropriately.

並列關鍵字

Intelligent Sliding-mode Controller ； Neural Network Estimator ； Impedance Control

參考文獻

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[4] R. Colombo et. al, "Robotic Techniques for Upper Limb Evaluation and Rehabilitation of Stroke Patients," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 3, 2005, pp. 311-324.

[9] S. Gopinath, I. N. Kar and R. K. P. Bhatt, " Associated Hermite series based Iterative Learning Control with Experience Inclusion using Local Learning Approach," 2006 International Joint Conference on Neural Networks, BC, Canada, 2006, pp. 4476-4483.

[10] Tsu-Tian Lee and Jin-Tsong Jeng, "The Chebyshev-Polynomials-Based Unified Model Neural Networks for Function Approximation," IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 28, no. 6, 1998, pp. 925-935.

國際替代計量

被動與主動運動之單自由度下肢復健系統

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