Hybrid position/force control of Stewart Manipulator using Extended Adaptive Fuzzy Sliding Mode Controller (E-AFSMC)

Area of Science:

• Robotics
• Control Systems Engineering
• Mechatronics

Background:

• Stewart Manipulators (SM) require sophisticated control for precise hybrid position/force tasks.
• Accurate estimation of contact parameters is crucial for effective force control in robotic manipulation.
• Existing control methods struggle with uncertainties and disturbances common in real-world applications.

Purpose of the Study:

• To develop and validate a new, effective hybrid position/force control strategy for Stewart Manipulators.
• To integrate contact parameter estimation with advanced adaptive control for enhanced performance.
• To address challenges posed by actuator saturation, large disturbances, and state-dependent uncertainties.

Main Methods:

• Utilizing the Hunt-Crossley nonlinear model to represent normal contact forces.
• Employing the Modified Extended Kalman Filter (MEKF) for real-time estimation of contact parameters.
• Designing an Extended Adaptive Fuzzy Sliding Mode Controller (E-AFSMC) for robust hybrid control.

Main Results:

• Numerical simulations demonstrate the effectiveness of the proposed control method.
• The controller successfully manages actuator saturation and unexpected large disturbances.
• The approach shows resilience against state-dependent uncertainties in the system.

Conclusions:

• The proposed hybrid position/force control method offers a robust and effective solution for Stewart Manipulators.
• Integration of MEKF for parameter estimation and E-AFSMC for control enhances system performance under challenging conditions.
• This research contributes a validated approach for advanced robotic manipulation tasks requiring precise force and position control.