An intelligent control method based on fuzzy logic for a robotic testing system for the human spine

Area of Science:

• Biomechanics
• Robotics
• Control Systems Engineering

Background:

• Previous hybrid controllers for human spine biomechanical studies exhibited inaccuracies due to measurement errors in position and force.
• These limitations hindered precise investigation of load-displacement characteristics.

Purpose of the Study:

• To develop and implement an alternative control strategy for robotic testing systems used in human spine biomechanics.
• To enhance the accuracy and robustness of load-displacement measurements in spinal testing.

Main Methods:

• A fuzzy logic controller (FLC) was developed and implemented in a robotic testing system.
• The FLC utilizes force difference and change in force difference as input parameters, with displacement as the output.
• A rule-table was designed based on these parameters for the FLC, emulating human operator knowledge through action rules.

Main Results:

• The FLC demonstrated improved performance compared to previous methods in experiments on a physical spring model.
• The fuzzy logic approach provided robust solutions capable of handling a wide range of system parameters and disturbances.
• The controller effectively addressed the limitations caused by measurement errors inherent in hybrid control systems.

Conclusions:

• Fuzzy logic control offers a viable and superior alternative to hybrid control for human spine biomechanical testing.
• The developed FLC enhances the precision and reliability of robotic testing systems for spinal research.
• This approach represents a heuristic and modular method for nonlinear, table-based control in biomechanical applications.