Posts Tagged Adaptive control

[ARTICLE] Assist-as-needed control strategy for upper-limb rehabilitation based on subject’s functional ability – Full Text

The assist-as-needed technique in robotic rehabilitation is a popular technique that encourages patients’ active participation to promote motor recovery. It has been proven beneficial for patients with functional motor disability. However, its application in robotic therapy has been hindered by a poor estimation method of subjects’ functional or movement ability which is required for setting the appropriate robotic assistance. Moreover, there is also the need for consistency and repeatability of the functional ability estimation in line with the clinical procedure to facilitate a wider clinical adoption. In this study, we propose a new technique of estimation of subject’s functional ability based on the Wolf Motor Function Test. We called this estimation the functional ability index. The functional ability index enables the modulation of robotic assistance and gives a more consistent indication of subjects’ upper-limb movement ability during therapy session. Our baseline controller is an adaptive inertia-related controller, which is integrated with the functional ability index algorithm to provide movement assistance as when needed. Experimental studies are conducted on three hemiplegic patients with different levels of upper-limb impairments. Each patient is requested to perform reaching task of lifting a can from table-to-mouth according to the guidelines stipulated in the Wolf Motor Function Test. Data were collected using two inertial measurement unit sensors installed at the flexion/extension joints, and the functional ability score of each patient was rated by an experienced therapist. Results showed that the proposed functional ability index algorithm can estimate patients’ functional ability level consistently with clinical procedure and can modify generated robotic assistance in accordance with patients’ functional movement ability.

The assist-as-needed (AAN) robotic strategy is a popular strategy for encouraging patients’ active participation in robot-assisted rehabilitation therapy. Numerous clinical outcomes have suggested the effectiveness of the AAN scheme to induce neuroplasticity in patients with neurological impairment.1 The AAN strategy focuses on providing the minimal amount of robotic assistance necessary for a patient to complete a movement,2 thus a significant effort is required from the patient. If the patient can perform the task flawlessly, robotic assistance is withdrawn. However, if the patient cannot complete the given task, assistance is offered only as much as it is needed.3

Deploying robotic assistance in accordance with the AAN strategy still come with many shortcomings.3,4 One major issue is how to appropriately estimate patients’ functional ability to set the correct level of robotic assistance. Another issue is the consistency of the estimated subject’s functional ability with clinical data and the repeatability across a wide range of subjects. An appropriate estimation of subject’s functional ability consistent with clinical data can give a realistic basis for deploying robotic assistance, since it gives a measure of subject’s actual disability level or recovery progress.5,6

A few strategies of AAN have been proposed recently which have attempted to address the challenges in the scheme. Wolbrecht et al.7 proposed a model-based robotic assistance strategy which can enable a robot to learn the patients’ ability in real time based on a radial-basis function (RBF). The RBF is applied under an adaptive control framework.

Another AAN strategy was proposed by Pehlivan et al.3 The authors introduced a minimal assist-as-needed (mAAN) strategy which uses a Kalman filter to estimate subjects’ functional inputs instead of the RBF technique that is a sensor-less force estimation strategy. Under the scheme, the ANN strategy is achieved in the following two ways: (1) by updating the derivative feedback gain to modify the bounds of allowable error on the desired trajectory and (2) by decaying a feed-forward disturbance rejection term which reduces the constraint on allowable quick movements. The combined effect could vary the robotic assistance according to the subjects’ capability.8 The potential limitation of this approach is the reliance on the robot model for the estimation of subject’s capability. It is well known that model errors always exist and can significantly excite the disturbance term making it difficult to accurately estimate the subject’s input. There is the implication that different robot models would produce different functional ability estimates which will hinder an appropriate standardization or deployment of robotic assistance for clinical purpose.9,10

Pérez-Rodríguez et al.11 also introduced an AAN strategy called anticipatory assistance-as-needed control algorithm capable of ensuring that the deviation from a patients’ desired trajectory is restored by giving an anticipated force assistance. This way, robotic assistance is always given as a restoring force to maintain the subject on the reference (desired) trajectory. With regards to the validity of this strategy, there are however no experimental studies till date.

Other noteworthy AAN strategies include the rule-based assistive strategy proposed by Wang et al.,12 which is applied in Physiotherabot; the hybrid impedance control for wrist and forearm rehabilitation proposed by Akdoğan and Adli,13 which is applied on a 3-degree-of-freedom (3-DOF) upper-limb rehabilitation robot; and the visual error augmentation-based AAN proposed by Akdoğan et al.,14 which can provide robotic assistance as needed by amplifying tracking error to heighten the participant’s motivation.

Efforts in developing an appropriate estimation strategy for AAN robotic assistance are still on course;15 however, there has been less focus on developing appropriate estimation techniques of subject’s functional ability that are consistent with the clinical procedure and that can be integrated in the control loop to provide robotic assistance.15,16

In this paper, we propose an ANN strategy to direct robotic assistance based on a novel functional ability index (FAI). The main originality of this work is the derivation of the new FAI estimation algorithm in accordance with the clinical procedure for the estimation of subject’s motor ability in movement task. As a preliminary investigation, we derive our FAI following the Wolf Motor Function Test (WMFT), a popular motor function test with consistency over a wide range of neurologically impaired patients. The FAI serves as input to a decay algorithm under the adaptive control law which consequently varies the robotic assistance according to the subject’s functional ability. The FAI is independent on the robot model or controller adaptation law and thus it is unaffected by modelling uncertainties.

The rest of the paper is organized as follows: section ‘System dynamic and control’ presents the dynamics for the robotic rehabilitation system, the proposed FAI, and the proposed control algorithm. Section ‘Experimental study’ presents the data collection and simulation study; section ‘Results’ describes the results; section ‘Discussion’ presents the discussion; and section ‘Conclusion’ concludes the paper.

The mechanical system

The proposed prototype of the exoskeleton system is shown in Figure 1. The system is an upper-limb rehabilitation robotic device with two active degrees of freedom (DOFs) at the shoulder and elbow joint, respectively. If actively controlled, the exoskeleton can permit abduction/adduction (AA) movement of the shoulder joint and flexion/extension (FE) movement of the elbow, thus allowing the possibility of performing the table to mouth reaching task.


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Figure 1. Exoskeleton device is of 2 degrees of freedom (DOFs).

Continue —> Assist-as-needed control strategy for upper-limb rehabilitation based on subject’s functional ability – Shawgi Younis Ahmed Mounis, Norsinnira Zainul Azlan, Fatai Sado,

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[Abstract + References] Cartesian Sliding Mode Control of an Upper Extremity Exoskeleton Robot for Rehabilitation

Abstract

Rehabilitation robots play an important role in rehabilitation treatment. Unlike conventional rehabilitation approach, the rehabilitation robotics provides an intensive rehabilitation motion with different modes (passive, active and active-assisted) based on the ability of the exoskeleton robot to perform assistive motion for a long period. However, this technology is still an emerging field. In this chapter, we present a Cartesian adaptive control based on a robust proportional sliding mode combined with time delay estimation for controlling a redundant exoskeleton robot called ETS-MARSE subject to uncertain nonlinear dynamics and external forces. The main objective of this research is to allow the exoskeleton robot to perform both rehabilitation modes, passive and active assistive motions with real subjects. The stability of the closed loop system is solved systematically, ensuring asymptotic convergence of the output tracking errors. Experimental results confirm the efficiency of the proposed control to provide an excellent performance despite the presence of dynamic uncertainties and external disturbances.

References

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via Cartesian Sliding Mode Control of an Upper Extremity Exoskeleton Robot for Rehabilitation | SpringerLink

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[Abstract] Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton

Highlights

    Adaptive integral sliding mode control design for exoskeletons.

    Finite time convergence of the closed-loop system.

    Robustness of the control law with respect to parametric variations and disturbances.

    No requirement of the knowledge of the system bounds.

    Real experiments using an upper limb exoskeleton with and without human subjects.

Abstract

A robust adaptive integral terminal sliding mode control strategy is proposed in this paper to deal with unknown but bounded dynamic uncertainties of a nonlinear system. This method is applied for the control of upper limb exoskeleton in order to achieve passive rehabilitation movements. Indeed, exoskeletons are in direct interaction with the human limb and even if it is possible to identify the nominal dynamics of the exoskeleton, the subject’s limb dynamics remain typically unknown and defer from a person to another. The proposed approach uses only the exoskeleton nominal model while the system upper bounds are adjusted adaptively. No prior knowledge of the exact dynamic model and upper bounds of uncertainties is required. Finite time stability and convergence are proven using Lyapunov theory. Experiments were performed with healthy subjects to evaluate the performance and the efficiency of the proposed controller in tracking trajectories that correspond to passive arm movements.

 

via Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton – ScienceDirect

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[Book Chapter] User Intention Driven Adaptive Gait Assistance Using a Wearable Exoskeleton – Springer


Abstract

A user intention based rehabilitation strategy for a lower-limb wearable robot is proposed and evaluated. The control strategy, which involves monitoring the human-orthosis interaction torques, determines the gait initiation instant and modifies orthosis operation for gait assistance, when needed. Orthosis operation is classified as assistive or resistive in function of its evolution with respect to a normal gait pattern. The control algorithm relies on the adaptation of the joints’ stiffness in function of their interaction torques and their deviation from the desired trajectories. An average of recorded gaits obtained from healthy subjects is used as reference input. The objective of this work is to develop a control strategy that can trigger the gait initiation from the user’s intention and maintain the dynamic stability, using an efficient real-time stiffness adaptation for multiple joints, simultaneously maintaining their synchronization. The algorithm has been tested with five healthy subjects showing its efficient behavior in initiating the gait and maintaining the equilibrium while walking in presence of external forces. The work is performed as a preliminary study to assist patients suffering from incomplete Spinal cord injury and Stroke.

Source: User Intention Driven Adaptive Gait Assistance Using a Wearable Exoskeleton – Springer

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[ARTICLE] A Subject-Adaptive Controller for Wrist Robotic Rehabilitation

…In order to derive maximum benefit from robot-assisted rehabilitation, it is critical that the implemented control algorithms promote the participant’s active engagement in therapy. Assist-as-needed (AAN) controllers address this need by providing only appropriate assistance during movement execution. Often, these controllers depend on the definition of an optimal movement profile, against which the participant’s movements are compared. In this paper, we present a novel subject-adaptive controller, consisting of two main components: AAN control algorithm and online trajectory recalculation…

via IEEE Xplore Abstract – A Subject-Adaptive Controller for Wrist Robotic Rehabilitation.

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