Neural network-based adaptive command filtering control for pneumatic artificial muscle robots with input uncertainties

Liu, Gendi; Sun, Ning; Liang, Dingkun; Chen, Yiheng; Yang, Tong; Fang, Yongchun

doi:10.1016/j.conengprac.2021.104960

Cited by 13 publications

(4 citation statements)

References 54 publications

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“…The growth topics of artificial muscle research in the last 2 years are SMA, twisted fiber actuator, CNTs, piezoelectric actuator, etc. Liquid crystal elastomer is one of the directions that deserve special attention ( Lee et al, 2022 ; Liu G et al, 2022 ). The evolution of artificial muscle applications is relatively smooth and bionic arm patent applications show a declining trend.…”

Section: Resultsmentioning

confidence: 99%

Advances in artificial muscles: A brief literature and patent review

Jing

Su²,

Yu³

et al. 2023

Front. Bioeng. Biotechnol.

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Background: Artificial muscles are an active research area now.Methods: A bibliometric analysis was performed to evaluate the development of artificial muscles based on research papers and patents. A detailed overview of artificial muscles’ scientific and technological innovation was presented from aspects of productive countries/regions, institutions, journals, researchers, highly cited papers, and emerging topics.Results: 1,743 papers and 1,925 patents were identified after retrieval in Science Citation Index-Expanded (SCI-E) and Derwent Innovations Index (DII). The results show that China, the United States, and Japan are leading in the scientific and technological innovation of artificial muscles. The University of Wollongong has the most publications and Spinks is the most productive author in artificial muscle research. Smart Materials and Structures is the journal most productive in this field. Materials science, mechanical and automation, and robotics are the three fields related to artificial muscles most. Types of artificial muscles like pneumatic artificial muscles (PAMs) and dielectric elastomer actuator (DEA) are maturing. Shape memory alloy (SMA), carbon nanotubes (CNTs), graphene, and other novel materials have shown promising applications in this field.Conclusion: Along with the development of new materials and processes, researchers are paying more attention to the performance improvement and cost reduction of artificial muscles.

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Section: Resultsmentioning

confidence: 99%

Advances in artificial muscles: A brief literature and patent review

Jing

Su²,

Yu³

et al. 2023

Front. Bioeng. Biotechnol.

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show abstract

“…Consider the TORA system described by ( 1) and the feedback cascade model ( 3) through its global change of coordinates (2). For any bounded initial conditions, the proposed controller (26) with the virtual controller ( 12), command filter ( 5), the parameters update laws ( 19), (33), and error compensating dynamics (7) can ensure that the tracking errors of the closed-loop controlled system converge to a small neighborhood of the origin by suitably choosing the design parameters, and all variables of the control systems remain bounded.…”

Section: Stablization Analysismentioning

confidence: 99%

“…If so, the system is stable. The η 2 , Γ 2 and η 4 , Γ 4 can affect the NN weight update rate ( 19) and (33).…”

Section: Stablization Analysismentioning

confidence: 99%

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Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Guo

Liu

2023

IEEE Access

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An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.

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Sliding mode control of antagonistically coupled pneumatic artificial muscles using radial basis neural network function

Nguyen,

Pham,

Nguyen

et al. 2023

SN Appl. Sci.

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This study presents a novel approach to enhance the control of Pneumatic Artificial Muscle (PAM) systems by combining Sliding Mode Control (SMC) with the Radial Basis Function Neural Network (RBFNN) algorithm. PAMs, when configured antagonistically, offer several advantages in creating human-like actuators. However, their inherent nonlinearity and uncertainty pose challenges for achieving precise control, especially in rehabilitation applications where control quality is crucial for safety and efficacy. To address these challenges, we propose an RBF-SMC approach that leverages the nonlinear elimination capability of SMC and the adaptive learning ability of RBFNN. The integration of these two techniques aims to develop a robust controller capable of effectively dealing with the inherent disadvantages of PAM systems under various operating conditions. The suggested RBF-SMC approach is theoretically verified using the Lyapunov stability theory, providing a solid foundation for its effectiveness. To validate its performance, extensive multi-scenario experiments were conducted, serving as a significant contribution of this research. The results demonstrate the superior performance of the proposed controller compared to conventional controllers in terms of convergence time, robustness, and stability. This research offers a significant contribution to the field of PAM system control, particularly in the context of rehabilitation. The developed RBF-SMC approach provides an efficient and reliable solution to overcome the challenges posed by PAMs’ nonlinearity and uncertainty, enhancing control quality and ensuring the safety and efficacy of these systems in practical applications.

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Neural network-based adaptive command filtering control for pneumatic artificial muscle robots with input uncertainties

Cited by 13 publications

References 54 publications

Advances in artificial muscles: A brief literature and patent review

Advances in artificial muscles: A brief literature and patent review

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Sliding mode control of antagonistically coupled pneumatic artificial muscles using radial basis neural network function

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