A Model-Free Redundant Manipulator Control Scheme Based on Tikhonov Regularization Cerebellar-Inspired Network

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence in Engineering

Background:

• Motion control accuracy in engineering is degraded by sensor noise and model uncertainties.
• Existing model-based control schemes lack robustness and adaptability in practical scenarios.
• Redundant manipulators present challenges due to their complex kinematic and dynamic properties.

Purpose of the Study:

• To propose a novel model-free Tikhonov-regularized cerebellum-inspired (MFTRC) control scheme.
• To enhance motion control accuracy, robustness, and adaptability in practical engineering.
• To mitigate measurement noise and avoid reliance on precise manipulator models.

Main Methods:

• Development of a Tikhonov-regularized cerebellum-inspired (TRC) network for model-free adaptive control.
• Integration of a neural dynamics (NDs) solver to handle kinematic constraints and generate reference signals.
• Formulation of the MFTRC scheme by combining the TRC network and ND solver for velocity-level control.

Main Results:

• The proposed MFTRC scheme effectively mitigates measurement noise and model uncertainties.
• Simulations and physical experiments demonstrate superior accuracy and robustness compared to existing methods.
• The control scheme exhibits strong adaptability for trajectory-tracking tasks in redundant manipulators.

Conclusions:

• The MFTRC control scheme offers a feasible and effective solution for motion control challenges.
• The cerebellum-inspired approach provides significant improvements in robustness and adaptability.
• This novel method advances model-free adaptive control for practical engineering applications.