An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle

Ba, Dang Xuan; Dinh, Truong Quang; Ahn, Kyoung Kwan

doi:10.1109/tmech.2016.2558292

Cited by 65 publications

(31 citation statements)

References 47 publications

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“…It is a regression design method combining the selection of Lyapunov functions with the design of the controller. e backstepping method has been applied extensively because of the unique superiority in dealing with the nonlinear control problem [18][19][20][21][22][23]. It begins with the lowest order differential equations of the system, introduces the concept of virtual control, and designs the virtual control to meet the requirements step by step.…”

Section: Backstepping Sliding Mode Robustmentioning

confidence: 99%

Backstepping Sliding Mode Robust Control for a Wire-Driven Parallel Robot Based on a Nonlinear Disturbance Observer

Wang

Lin

Zhou

et al. 2020

Mathematical Problems in Engineering

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Based on a nonlinear disturbance observer, a backstepping sliding mode robust control is proposed for a wire-driven parallel robot (WDPR) system used in the wind tunnel test to dominate the motion of the end effector. The control method combines both the merits of backstepping control and sliding mode robust control. The WDPR is subject to different types of disturbances, and these disturbances will affect the motion precision of the end effector. To overcome these problems, a nonlinear disturbance observer (NDO) is designed to reject such disturbances. In this study, the design method of the nonlinear disturbance observer does not require the reliable dynamic model of the WDPR. Moreover, the design method can be used not only in the WDPR but also in other parallel robots. Then, a backstepping design method is adopted and a sliding mode term is introduced to construct a desired controller, and the disturbances are compensated in the controller to reduce the switching gain and guarantee the robustness. For the sake of verifying the stabilization of the closed-loop system, the Lyapunov function is constructed to analyze the stabilization of the system. Finally, the feasibility and validity of the proposed control scheme are proved through both simulation and experimental results.

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Section: Backstepping Sliding Mode Robustmentioning

confidence: 99%

Backstepping Sliding Mode Robust Control for a Wire-Driven Parallel Robot Based on a Nonlinear Disturbance Observer

Wang

Lin

Zhou

et al. 2020

Mathematical Problems in Engineering

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“…Fortunately, neural networks (NN) have been used in controller design for their excellent ability to approximate arbitrary unknown nonlinearities [21], including the unmodeled dynamics and random disturbances. Therefore, NN controllers have been designed for high nonlinear pneumatic systems to obtain better model compensation [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

2019

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In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.

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“…Another common way to identify the model of PAM-based actuator is the grey-box experiment method [18][19][20][21]. In 2015, to deal with uncertain nonlinearity of PAMs, Dang Xuan Ba et al introduced a grey-box experimental model, which consisted of uncertain, unknown, and nonlinear terms.…”

Section: Introductionmentioning

confidence: 99%

“…In 2015, to deal with uncertain nonlinearity of PAMs, Dang Xuan Ba et al introduced a grey-box experimental model, which consisted of uncertain, unknown, and nonlinear terms. Based on the built-in model, the authors employed a sliding mode control strategy [18] and an integrated intelligent nonlinear control approach [19] for the tracking purpose. The control performance was significantly improved, and the system could track the 10 • amplitude sinusoidal signal with 1.5 Hz frequency.…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Fractional Order Integral Sliding Mode Control of an Antagonistic Actuator Driven by Pneumatic Artificial Muscles

2019

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Recently, pneumatic artificial muscles (PAMs), a lightweight and high-compliant actuator, have been increasingly used in assistive rehabilitation robots. PAM-based applications must overcome two inherent drawbacks. The first is the nonlinearity due to the compressibility of the air, and the second is the hysteresis due to its geometric construction. Because of these drawbacks, it is difficult to construct not only an accurate mathematical model but also a high-performance control scheme. In this paper, the discrete-time fractional order integral sliding mode control approach is investigated to deal with the drawbacks of PAMs. First, a discrete-time second order plus dead time mathematical model is chosen to approximate the characteristics of PAMs in the antagonistic configuration. Then, the fractional order integral sliding mode control approach is employed together with a disturbance observer to improve the trajectory tracking performance. The effectiveness of the proposed control method is verified in multi-scenario experiments using a physical actuator.

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An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle

Cited by 65 publications

References 47 publications

Backstepping Sliding Mode Robust Control for a Wire-Driven Parallel Robot Based on a Nonlinear Disturbance Observer

Backstepping Sliding Mode Robust Control for a Wire-Driven Parallel Robot Based on a Nonlinear Disturbance Observer

High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

Discrete-Time Fractional Order Integral Sliding Mode Control of an Antagonistic Actuator Driven by Pneumatic Artificial Muscles

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