A Visual Servo-Based Predictive Control With Echo State Gaussian Process for Soft Bending Actuator

Yu Cao

doi:10.1109/TMECH.2020.3042774

A Visual Servo-Based Predictive Control With Echo State Gaussian Process for Soft Bending Actuator

2020-01-01

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DOI：10.1109/TMECH.2020.3042774

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This article designed a soft bending actuator (SBA) and presented a neural-network-based tracking control strategy for such an actuator. To achieve high-precision control, a visual feedback system was established by using a high-speed camera to dynamically compute the corresponding central angle which described the curvature of the SBA. Considering its nonlinear features, the echo state Gaussian process, a fusion of echo state network and Gaussian process, was used to approximate the SBA's dynamics with a nonlinear autoregressive exogenous model. On this basis, a single-layer neural network was trained to calculate the control signal in light of the idea of predictive control, due to its simplicity and good approximation capabilities. The stability of the closed-loop system was guaranteed, and experimental results indicated that the proposed strategy showed relatively high tracking accuracy under various reference trajectories.

Keywords:

Bending

Actuators

Electron tubes

Rubber

Muscles

Feature extraction

Visualization

Echo state Gaussian process (ESGP)

soft bending actuator (SBA)

single-layer neural network

visual feedback

A Visual Servo-Based Predictive Control With Echo State Gaussian Process for Soft Bending Actuator

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