Switching Model Predictive Control for Thin McKibben Muscle Servo Actuator

Dynamic characteristics and control of thin McKibben muscle (TMM) have not yet been fully investigated, especially on the translational antagonistic pair system. Therefore, the objective of this study is to propose a Switching Model Predictive Control (SMPC) based on a Piecewise Affine (PWA) system model to control a translational antagonistic-pair TMM servo actuator. A novel configuration enables the servo actuator to achieve a position control of 40 mm within a small footprint. The result shows that the feedback system gives minimal steady-state errors when tracking staircase and setpoint references ranging from 0 to 3.5 cm. The controller also produces better transient and steady-state responses than our previously developed Gain-scheduled Proportional-Integral-Derivative (GSPID) controller. The evidence from this study suggests that a predictive control for a TMM servo actuator is feasible.

Automated summary

This study proposes a Switching Model Predictive Control based on a Piecewise Affine system model for a translational antagonistic-pair TMM servo actuator. The results show that this control method has minimal steady-state errors and better transient and steady-state responses compared to the previously developed Gain-scheduled Proportional-Integral-Derivative controller.