Control of elastic joint robot based on electromyogram signal by pre-trained Multi-Layer Perceptron

Souzanchi-K, Mahdi; Owhadi-Kareshk, Moein; T., Mohammad-R. Akbarzadeh

doi:10.1109/ijcnn.2016.7727891

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…There are some theories based on this strategy, such as adaptive fuzzy control [25], robust control [26], impedance control [27], and adaptive control [28]. Souzanchi-K et al [29] also used this general strategy in a human-robot interaction application in which a Multi-Layer Perceptron (MLP) is pre-trained to match EMG signals to the kinematic data of a human hand's movement. However, their voltage-based controller requires the exact actuator dynamics, which may either be unavailable or change in time.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast with [29], here, we aim for a robust adaptive control strategy. Accordingly, we propose a voltage-controlled Adaptive Fuzzy Sliding Mode Controller (AFSMC) for elastic joint robot arms guided by EMG signals.…”

Section: Introductionmentioning

confidence: 99%

“…The closed-loop system's stability is guaranteed following a Lyapunov analysis of the sliding mode framework, despite the high model complexities, parameter uncertainty, and noisy measurements. Compared to [29], results show that the proposed strategy is more robust against uncertainties such as random time delay and un-modeled dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Fuzzy Robust Tracking Control Using Human Electromyogram Signals for Elastic Joint Robots

Souzanchi-K¹,

Akbarzadeh-T²,

Naghavi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Sliding mode control is often used for systems with parametric uncertainties due to its desirable robustness and stability, but this approach carries undesirable chattering. Similarly, joint elasticity is a common phenomenon induced by transmission systems in robots, but it presents additional complexity in robot dynamics that could lead to robot vibrations or even instability. Coupling these two phenomena presents further compounded challenges, particularly when faced with the human interface's added uncertainties. Here, a stable voltage-based adaptive fuzzy strategy to sliding mode control is proposed for an elastic joint robot arm that uses a human's upper limb electromyogram (EMG) signals to direct its movement. The concurrent use of EMG with the elastic joint arm provides a suitable framework for human-robot interaction. EMG signals represent human's 'intention' on motion, i.e., they move between 50-100 ms before the mechanical motion begins. Hence this strategy potentially builds better synchronization between the robot and human intention. Furthermore, the adaptive fuzzy strategy eliminates the system chattering while also providing robustness against parametric uncertainties and time delay. Lyapunov analysis also shows bounded-input bounded-output stability of the closed-loop system. Finally, the proposed approach is experimentally implemented in the Sport Science Research Institute. Comparisons with a competing strategy, as well as a torque mode controller, shows that the proposed approach leads to a computationally faster and more accurate controller.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Fuzzy Robust Tracking Control Using Human Electromyogram Signals for Elastic Joint Robots

Souzanchi-K¹,

Akbarzadeh-T²,

Naghavi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…According to Silva, Spatti and Flauzino [17], it is considered a powerful and quite versatile tool and can be applied in the solution of problems related to a wide range of areas of knowledge, such as universal approximation of functions, pattern recognition processes, identification and control, prediction of time series, and optimization of systems. ANNs are widely applied in biomedical studies, as in Lima et al [10], Souzanchi-K, Owhadi-Kareshk and Akbarzadeh-T [19], Baracho et al [20], Bevilacqua et al [21], Barizão et al [15], and Silva et al [22].…”

Section: Introductionmentioning

confidence: 99%

Wavelet packet transform and multilayer perceptron to identify voices with a mild degree of vocal deviation

Morikawa

Spatti

Dájer

2022

Rev. Investig. Innov. Cienc. Salud

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Introduction. Laryngeal disorders are characterized by a change in the vibratory pattern of the vocal folds. This disorder may have an organic origin described by anatomical fold modification, or a functional origin caused by vocal abuse or misuse. The most common diagnostic methods are performed by invasive imaging features that cause patient discomfort. In addition, mild voice deviations do not stop the individual from using their voices, which makes it difficult to identify the problem and increases the possibility of complications. Aim. For those reasons, the goal of the present paper was to develop a noninvasive alternative for the identification of voices with a mild degree of vocal deviation applying the Wavelet Packet Transform (WPT) and Multilayer Perceptron (MLP), an Artificial Neural Network (ANN). Methods. A dataset of 74 audio files were used. Shannon energy and entropy measures were extracted using the Daubechies 2 and Symlet 2 families and then the processing step was performed with the MLP ANN. Results. The Symlet 2 family was more efficient in its generalization, obtaining 99.75% and 99.56% accuracy by using Shannon energy and entropy measures, respectively. The Daubechies 2 family, however, obtained lower accuracy rates: 91.17% and 70.01%, respectively. Conclusion. The combination of WPT and MLP presented high accuracy for the identification of voices with a mild degree of vocal deviation.

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Brain emotional learning impedance control of uncertain nonlinear systems with time delay: Experiments on a hybrid elastic joint robot in telesurgery

Souzanchi-K

Akbarzadeh-T

2021

Computers in Biology and Medicine

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Control of elastic joint robot based on electromyogram signal by pre-trained Multi-Layer Perceptron

Cited by 6 publications

References 17 publications

Adaptive Fuzzy Robust Tracking Control Using Human Electromyogram Signals for Elastic Joint Robots

Adaptive Fuzzy Robust Tracking Control Using Human Electromyogram Signals for Elastic Joint Robots

Wavelet packet transform and multilayer perceptron to identify voices with a mild degree of vocal deviation

Brain emotional learning impedance control of uncertain nonlinear systems with time delay: Experiments on a hybrid elastic joint robot in telesurgery

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