Robust two-scale command shaping for residual vibration mitigation in nonlinear systems

Wilbanks, J. Justin; Leamy, Michael J.

doi:10.1016/j.jsv.2019.114927

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The hardware system includes flexible fabric electrodes, signal sensing front-end, an acquisition processing module, etc. The flexible fabric electrodes are arranged side-by-side on the flexible fabric cuff for multi-channel signal acquisition, and the sEMG signal enters the sensing front-end through the flexible fabric electrodes, and after filtering, amplifying, and other functional circuits, the acquisition processing module performs A/D acquisition and data processing ( Wilbanks and Leamy, 2019 ; Jiang et al, 2019 ). The acquisition and processing module includes data sampling, wireless transmission, a sensing circuit, and power management.…”

Section: Wearable Flexible Bionic Gesture Surface Muscle Signal Acqui...mentioning

confidence: 99%

Multi-Stream Convolutional Neural Network-Based Wearable, Flexible Bionic Gesture Surface Muscle Feature Extraction and Recognition

Liu¹,

Lu²

2022

Front. Bioeng. Biotechnol.

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Surface electromyographic (sEMG) signals are weak physiological electrical signals, which are highly susceptible to coupling external noise and cause major difficulties in signal acquisition and processing. The study of using sEMG signals to analyze human motion intention mainly involves data preprocessing, feature extraction, and model classification. Feature extraction is an extremely critical part; however, this often involves many manually designed features with specialized domain knowledge, so the experimenter will spend time and effort on feature extraction. To address this problem, deep learning methods that can automatically extract features are applied to the sEMG-based gesture recognition problem, drawing on the success of deep learning for image classification. In this paper, sEMG is captured using a wearable, flexible bionic device, which is simple to operate and highly secure. A multi-stream convolutional neural network algorithm is proposed to enhance the ability of sEMG to characterize hand actions in gesture recognition. The algorithm virtually augments the signal channels by reconstructing the sample structure of the sEMG to provide richer input information for gesture recognition. The methods for noise processing, active segment detection, and feature extraction are investigated, and a basic method for gesture recognition based on the combination of multichannel sEMG signals and inertial signals is proposed. Suitable filters are designed for the common noise in the signal. An improved moving average method based on the valve domain is used to reduce the segmentation error rate caused by the short resting signal time in continuous gesture signals. In this paper, three machine learning algorithms, K-nearest neighbor, linear discriminant method, and multi-stream convolutional neural network, are used for hand action classification experiments, and the effectiveness of the multi-stream convolutional neural network algorithm is demonstrated by comparison of the results. To improve the accuracy of hand action recognition, a final 10 gesture classification accuracy of up to 93.69% was obtained. The separability analysis showed significant differences in the signals of the two cognitive-behavioral tasks when the optimal electrode combination was used. A cross-subject analysis of the test set subjects illustrated that the average correct classification rate using the pervasive electrode combination could reach 93.18%.

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Section: Wearable Flexible Bionic Gesture Surface Muscle Signal Acqui...mentioning

confidence: 99%

Multi-Stream Convolutional Neural Network-Based Wearable, Flexible Bionic Gesture Surface Muscle Feature Extraction and Recognition

Liu¹,

Lu²

2022

Front. Bioeng. Biotechnol.

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“…The closed-loop control schemes provide a good and robust performance, but they required installation of additional sensors, good mathematical model of the system and were difficult to implement. Besides, open loop control approaches that include input shaping [18,19,20], command smoothing [21,22] and command filtering [23] techniques have also been reported. The control approach is easy to implement and does not need additional sensors.…”

Section: Introductionmentioning

confidence: 99%

Adaptive input shaper for payload swing control of a 5-DOF tower crane with parameter uncertainties and obstacle avoidance

Rehman

Mohamed

Husain

et al. 2023

Automation in Construction

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“…At present, there are many researches on physical components and control optimization methods for suppressing vibration of flexible manipulator (Diken, 2000;Jinqiao et al, 2010;Guo et al, 2016;Wilbanks and Leamy, 2019;Chen et al, 2021;Niu et al, 2021;Zhang et al, 2021). Korayem and Ghariblu (2004), Ghariblu and Korayem (2006), Korayem et al (2011), and Korayem et al (2013) proposed an open-loop optimal control method to generate the optimal trajectory of a flexible mobile manipulator in point-to-point motion, so that the robot can bear the maximum load between two designated terminal positions.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning of the Variable-Stiffness Flexible Manipulator Based on CADE Algorithm for Vibration Reduction Control

Cheng

Liu

et al. 2021

Front. Bioeng. Biotechnol.

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Robotic manipulators are widely used for precise operation in the medical field. Vibration suppression control of robotic manipulators has become a key issue affecting work stability and safety. In this paper an optimal trajectory planning control method to suppress the vibration of a variable-stiffness flexible manipulator considering the rigid-flexible coupling is proposed. Through analyzing the elastic deformation of the variable-stiffness flexible manipulator, a distributed dynamic physical model of the flexible manipulator is constructed based on the Hamilton theory. Based on the mathematical model of the system, the design of the vibration damping controller of the flexible manipulator is proposed, and the control system with nonlinear input is considered for numerical analysis. According to the boundary conditions, the vibration suppression effect of the conventional and the variable-stiffness flexible manipulator is compared. The motion trajectory of the variable-stiffness flexible manipulator and compare the vibration response from different trajectories. Then, with minimum vibration displacement, minimum energy consumption and minimum trajectory tracking deviation as performance goals, the trajectory planning of the variable-stiffness flexible manipulator movement is carried out based on the cloud adaptive differential evolution (CADE) optimization algorithm. The validity of the proposed trajectory planning method is verified by numerical simulation.

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Robust two-scale command shaping for residual vibration mitigation in nonlinear systems

Cited by 5 publications

References 24 publications

Multi-Stream Convolutional Neural Network-Based Wearable, Flexible Bionic Gesture Surface Muscle Feature Extraction and Recognition

Multi-Stream Convolutional Neural Network-Based Wearable, Flexible Bionic Gesture Surface Muscle Feature Extraction and Recognition

Adaptive input shaper for payload swing control of a 5-DOF tower crane with parameter uncertainties and obstacle avoidance

Optimal Trajectory Planning of the Variable-Stiffness Flexible Manipulator Based on CADE Algorithm for Vibration Reduction Control

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