Hysteresis Modelling of Pneumatic Artificial Muscle using General Cubic Equation and Factor Theorem Prediction Method / Mohd Azuwan Mat Dzahir...[et al.]

Due to inherent hysteresis in a pneumatic artificial muscle, the accompanying control of this compliant actuator becomes more complicated. In literature, only a few implementations of the hysteresis modelling in the associated position control system could be found. In addition, the high complexity of the contraction-force and contraction-pressure models when accounting for hysteresis effect, the implementation of such model is limited in its use in the pneumatic muscle position control only. However, implementing a complicated control algorithm does not always indicate the best solution that could be used to control the pneumatic artificial muscles. There are arguments in the field of rehabilitation robotics regarding what was the best control system to the orthotic problem for rehabilitation. It is preferred that the control system should be simplified as much as possible; multiple sensors and impedances are only increase the complexity of the control system. Rather than using a very complicated algorithm for control system of the pneumatic artificial muscle, a simple and noble prediction method using general cubic equation and factor theorem is proposed for the hysteresis modelling at different loads. The methodology used to establish the hysteresis modelling and prediction method of the pneumatic artificial muscle are as follows; first is the characterization of the pneumatic artificial muscle at different loads and pressures; second is to develop a prediction method for generating constraint models of hysteresis data at a different loads using general cubic equation and factor theorem; third is to establish a simple theorem or algorithm to extract the hysteresis models (i.e., contraction and expansion) of a pneumatic artificial muscle at different loads based on the generated constraint models; and the final stage of the research is to obtain the hysteresis models at different loads. The generated hysteresis models and hysteresis data obtained from experimental study were compared to verify the reliability of the proposed hysteresis modelling prediction method. The simulation results shows that the hysteresis modelling was able to generate an apropriate constraint models at a different loads.