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

Area of Science:

• Robotics
• Control Systems Engineering
• Computational Neuroscience

Background:

• Motion control accuracy is challenged by sensor noise and model uncertainties in engineering.
• Existing model-based control schemes lack robustness and adaptability in real-world scenarios.
• Human cerebellum's motor coordination offers inspiration for advanced control strategies.

Purpose of the Study:

• To propose a novel model-free Tikhonov-regularized cerebellum-inspired (MFTRC) control scheme.
• To enhance robustness and adaptability in motion control despite measurement noise and model uncertainties.
• To achieve accurate trajectory tracking for redundant manipulators without precise models.

Main Methods:

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

Main Results:

• The MFTRC scheme effectively mitigates measurement noise and avoids reliance on accurate manipulator models.
• Simulations and physical experiments demonstrated the scheme's feasibility and effectiveness.
• The proposed MFTRC control scheme exhibited superior accuracy and robustness compared to existing state-of-the-art methods.

Conclusions:

• The MFTRC control scheme offers a robust and adaptable solution for motion control in practical engineering.
• The cerebellum-inspired approach, combined with Tikhonov regularization and neural dynamics, advances model-free adaptive control.
• This novel scheme significantly improves trajectory-tracking performance in challenging environments.