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Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models.

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This study demonstrates how deep neural networks can model complex soft robot dynamics. This approach enables precise model-based control, significantly reducing steady-state errors in soft robotic systems.

Keywords:
DNNmachine learningmodel predictive controlsoft robot actuationsoft robot control

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Soft robots offer transformative human-robot interaction capabilities.
  • Accurate dynamic modeling of soft robots and actuators is crucial for model-based control but remains challenging.
  • Deep neural networks excel at modeling complex system dynamics.

Purpose of the Study:

  • To develop a novel method for model-based control of soft robots using deep neural networks.
  • To formulate a linearized discrete state-space representation from neural network gradients.
  • To implement model predictive control (MPC) for a six-degree-of-freedom pneumatic soft robot.

Main Methods:

  • Utilized deep neural networks to model the dynamics of a six-degree-of-freedom pneumatic soft robot.
  • Derived a linearized discrete state-space model from neural network gradients.
  • Implemented model predictive control (MPC) using the learned neural network model.

Main Results:

  • Achieved an average steady-state error of approximately 1° with integral control and 2° without.
  • The neural network-based model outperformed a first-principles model in steady-state error, rise time, and overshoot.
  • Demonstrated effective control of a complex soft robotic system.

Conclusions:

  • Deep neural networks provide a viable approach for modeling soft robot dynamics.
  • Combining empirical modeling with model-based control enhances performance for soft robots and actuators.
  • The proposed method shows significant potential for advancing soft robotics control.