MISO Model Free Adaptive Control of Single Joint Rehabilitation Robot Driven by Pneumatic Artificial Muscles

Li, Yang; Liu, Quan; Meng, Wei; Xie, Yuanlong; Ai, Qingsong; Xie, Sheng Quan

doi:10.1109/aim43001.2020.9158805

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Then, both sides of (4) become zero, and thus parameters k 1 and k 2 do not affect the steady-state characteristics. The mass flow rate equation (5) becomes αm in = (1 − α)m out . Therefore,P is obtained from u irrespective of A 0 .…”

Section: ) Joint Dynamicsmentioning

confidence: 99%

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Detailed Dynamic Model of Antagonistic PAM System and Its Experimental Validation: Sensorless Angle and Torque Control With UKF

Shin

Ibayashi²,

Kogiso

2022

IEEE/ASME Trans. Mechatron.

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This paper proposes a detailed nonlinear mathematical model of an antagonistic pneumatic artificial muscle (PAM) actuator system for estimating the joint angle and torque using an unscented Kalman filter (UKF). The proposed model is described in a hybrid state-space representation. It includes the contraction force of the PAM, joint dynamics, fluid dynamics of compressed air, mass flows of a valve, and friction models. A part of the friction models is modified to obtain a novel form of Coulomb friction depending on the inner pressure of the PAM. For model validation, offline and online UKF estimations and sensor-less tracking control of the joint angle and torque are conducted to evaluate the estimation accuracy and tracking control performance. The estimation error is less than 7.91 %, and the steady-state tracking control performance is more than 94.75 %. These results confirm that the proposed model is detailed and could be used as the state estimator of an antagonistic PAM system.

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Section: ) Joint Dynamicsmentioning

confidence: 99%

“…Studies have also investigated PAM control methods such as a sliding mode control [4], adaptive control [5], and guaranteedcost control [6] by considering the modeling uncertainty and high nonlinearity of PAM actuators.…”

Section: Introductionmentioning

confidence: 99%

Detailed Dynamic Model of Antagonistic PAM System and Its Experimental Validation: Sensorless Angle and Torque Control With UKF

Shin

Ibayashi²,

Kogiso

2022

IEEE/ASME Trans. Mechatron.

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“…Instead of decomposing it into several single pneumatic muscles, MISO-IMFAC controls the robot as an unknown model to enhance the control robustness. The control scheme is as follows, and more details of the MISO-IMFAC can be found in our previous work (Li Y. et al, 2020 ).…”

Section: Path Planning and Impedance Control For Coordinated Upper Limb Rehabilitationmentioning

confidence: 99%

Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

Liu

et al. 2021

Front. Neurorobot.

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The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow–wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of the upper limb joints, so as to generate adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the movement coordination of the patient and enhance the rehabilitation effectiveness.

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“…However, iterative pretraining is needed which is more suitable for repetitive applications such as rehabilitation exercises. In [16], an improved MFAC for PAM actuated systems for rehabilitation robots which did not require any pre-training is constructed. However, the controller could only manage reasonable tracking in low-speed systems.…”

Section: Introductionmentioning

confidence: 99%

Simple Neural Network Compact Form Model-Free Adaptive Controller for Thin McKibben Muscle System

et al. 2022

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This paper proposes a simple neural network compact form model-free adaptive controller (NNCFMFAC) for a single thin McKibben muscle (TMM) system. The main contribution of this work is the simplification of the current neural network (NN) based compact form model-free adaptive controller (CFMFAC), which requires only two adaptive weights. This is achieved by designing a NN topology specifically to enhance the CFMFAC response. The prominent control parameters of the CFMFAC are combined and one adaptive weight is used for self-tuning, while the second adaptive weight is used to minimize the offset at each operating point. Hence the issues of redundant adaptive weights in complex neuro based CFMFACs and slow response of the CFMFAC are significantly addressed. The idea is proven in three ways: analytically, simulation on a nonlinear system and experiments on a TMM platform. Experimental results demonstrate the superiority of the proposed method over the conventional CFMFAC is confirmed by a 76% improvement in the convergence speed and a 60% reduction in root mean square error (RMSE). It is envisaged that the proposed controller can be very useful for TMM driven applications as it is model independent, has fast response, high tracking accuracy, and minimal complexity.

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MISO Model Free Adaptive Control of Single Joint Rehabilitation Robot Driven by Pneumatic Artificial Muscles

Cited by 4 publications

References 19 publications

Detailed Dynamic Model of Antagonistic PAM System and Its Experimental Validation: Sensorless Angle and Torque Control With UKF

Detailed Dynamic Model of Antagonistic PAM System and Its Experimental Validation: Sensorless Angle and Torque Control With UKF

Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

Simple Neural Network Compact Form Model-Free Adaptive Controller for Thin McKibben Muscle System

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