Control of TCP muscles using Takagi-Sugeno-Kang fuzzy inference system

Inspired by nature, many types of artificial muscles or actuators have been developed for mechatronic systems. Twisted and coiled polymer (TCP) muscles are some examples that are made from nylon or polyethylene. The muscles contract over 20% strokes under considerable load. There are limited studies available on the modeling and control of these muscles for practical use. In this paper, we show discrete-time modeling and control of the force of the muscles. Prediction error method (PEM) was used for parameter estimation of discrete-time state space models to find the order of the model. Then, proportional-integral (PI) controller was demonstrated as a classical controller to regulate the force of the muscles. To increase the speed of actuation, a Takagi-Sugeno-Kang (TSK) controller was employed as a fuzzy controller. Our experimental results demonstrate how the muscle can be controlled in practical settings and shows the superiority of TSK over the PI controller. We anticipate that the model and controllers will add new knowledge for the use of the twisted and coiled polymer muscle in mechatronic system.