Active disturbance rejection control of a pneumatic artificial muscle-based parallel robot

Abstract Parallel robots, with their multiple chains connected to a common base, offer significant advantages over serial robots, such as increased stiffness, precision, and load-bearing capacity. These strengths have led to widespread use in areas like high-precision assembly and medical robotics. Recent advancements have focused on optimizing their performance through enhanced kinematic and dynamic modeling. Integrating pneumatic artificial muscles (PAMs) into parallel robots represents a promising innovation, combining the mechanical benefits of parallel kinematics with the unique properties of pneumatic actuation. PAMs, known for their high power-to-weight ratio, low maintenance, and compliance, are particularly suitable for applications requiring precise control, such as medical robotics and rehabilitation systems. Recent research has concentrated on addressing the challenges posed by the nonlinear and time-varying nature of PAMs through advanced control strategies, including adaptive and model-based techniques. This study investigates the control of a PAM-based parallel selective compliance assembly robot arm (SCARA) using an Active Disturbance Rejection Control (ADRC) strategy to improve joint-angle position accuracy. Experimental results validate the effectiveness of the ADRC approach, which outperforms the traditional Proportional-Integral-Derivative (PID) controller, demonstrating the potential of PAM-based parallel robots in precision-demanding applications, particularly in rehabilitation robotics.