Learning-Based Neural Adaptive Anti-Coupling Control for a Class of Robots Under Input and Structural Coupled Uncertainties Article Swipe

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Junlong Niu , Xiansheng Qin , Zheng Wang ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1109/access.2021.3060739

This paper investigates the learning - based adaptive anti-coupling control issue for the robots under input and structural coupled uncertainties. In this paper, the input and structural coupled uncertainties are modeled and transformed into a system state related term, an internal state related term and a system input related term. With the aid of the actual exponential input-state stability and the dynamic auxiliary signal, the internal state related uncertainties can be suppressed. By utilizing the properties of the robot dynamics and several special nonlinear functions, the system state related uncertainties can be handled. Moreover, to overcome the system input related uncertainties, an indirect control law and the adaptive boundary estimation law have been designed. To simplify the control structure, the neural networks have been introduced as online approximators. Finally, a novel learning-based intelligent adaptive anti-coupling control structure has been established for the robots. The simulation results revealed the satisfactory control performance of the proposed anti-coupling control algorithm.

Related Topics

Computer Science

Robot

Control Engineering

Artificial Intelligence

Mechanical Engineering

Mathematical Analysis

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Control theory (sociology) Computer science Robot Nonlinear system Adaptive control Coupling (piping) Artificial neural network Control engineering State (computer science) Boundary (topology) Stability (learning theory) Control (management) Artificial intelligence Engineering Mathematics Algorithm Machine learning Physics Quantum mechanics Mechanical engineering Mathematical analysis

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2021.3060739
PDF: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09359776.pdf
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References: 78
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DOI: https://doi.org/10.1109/access.2021.3060739

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Title: Learning-Based Neural Adaptive Anti-Coupling Control for a Class of Robots Under Input and Structural Coupled Uncertainties

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Language: en

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Publication year: 2021

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Publication date: 2021-01-01

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Authors: Junlong Niu, Xiansheng Qin, Zheng Wang

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Landing page: https://doi.org/10.1109/access.2021.3060739

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Concepts: Control theory (sociology), Computer science, Robot, Nonlinear system, Adaptive control, Coupling (piping), Artificial neural network, Control engineering, State (computer science), Boundary (topology), Stability (learning theory), Control (management), Artificial intelligence, Engineering, Mathematics, Algorithm, Machine learning, Physics, Quantum mechanics, Mechanical engineering, Mathematical analysis

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