Realization of a Simultaneous Position-Stiffness Controllable Antagonistic Joint Driven by Twisted-Coiled Polymer Actuators Using Model Predictive Control

Abstract

Stiffness adjustability is one of the key characteristics that help humans achieve safe and reliable motions. This paper aims to realize the simultaneous position-stiffness control of an antagonistic joint driven by a type of super-coiled polymer (SCP) artificial muscles, made from a combination of Spandex and nylon fibers. A nonlinear model that can exhibit the variable stiffness characteristics of the actuator is first proposed. The model, whose parameters are identified and verified experimentally, is suitable for estimating the actuator's stiffness as a function of the length and temperature. Based on a linearized model of the antagonistic joint system, a model predictive control (MPC) is applied to control joint angle and stiffness simultaneously. By controlling both actuators' temperatures, simultaneous position-stiffness control is realized. Our contribution also includes enhancement of MPC control by incorporating time delay estimation to estimate model variations and external disturbances. The control performance is verified with both step command and sinusoidal reference with composite frequencies of 0.02Hz to 0.1Hz. Experimental results show that the system can achieve high accuracy of joint position control performance with the maximum position error of 0.42 degrees while varying joint stiffness 46.5%.

Keywords

• Model predictive control
• spandex
• super-coiled polymer actuators
• soft actuators
• variable stiffness