Hybrid adaptive disturbance rejection control for inflatable robotic arms

Area of Science:

• Robotics
• Control Systems Engineering
• Mechatronics

Background:

• Inflatable robotic arms (IRAs) present significant control challenges due to their high nonlinearity and stochastic dynamics.
• Accurate modeling of IRAs is difficult, necessitating advanced control strategies that rely less on precise system identification.

Purpose of the Study:

• To address the controller design problem for highly nonlinear and stochastic inflatable robotic arms (IRAs).
• To develop a robust control scheme capable of handling the complex tracking control requirements of hard-to-model IRAs.

Main Methods:

• A novel hybrid adaptive disturbance rejection control (HADRC) scheme was devised.
• Model-free adaptive control (MFAC) was analytically enhanced for superlinear convergence using online input-output data.
• Active disturbance rejection control (ADRC) was employed to reject internal and external disturbances with minimal model information.
• Fuzzy logic control (FLC) was integrated to coordinate sub-controllers and ensure smooth robotic arm movements.

Main Results:

• The proposed HADRC scheme demonstrated superior performance in comparative simulations and experiments.
• Effective tracking control was achieved for a 2-degree-of-freedom (DOF) inflatable robotic arm.
• The control strategy successfully managed system nonlinearities and external/internal disturbances.

Conclusions:

• The HADRC offers a viable and effective solution for controlling complex inflatable robotic arms.
• The integration of MFAC, ADRC, and FLC provides a robust framework for achieving precise and smooth IRA motion.
• This research advances the control methodologies for soft robotic systems and underactuated manipulators.