Towards an efficient inverse static model of a Festo actuator made of two antagonist muscles for hybrid control of its position and stiffness
Bertrand Tondu (LAAS-GEPETTO)

TL;DR
This paper proposes a simplified static inverse model for a Festo antagonist air muscle actuator, aiming to facilitate hybrid position and stiffness control by mimicking neural control mechanisms.
Contribution
It introduces a simplified static model based on classic McKibben muscle theory to enable integrated position-stiffness control of Festo air muscles.
Findings
The model can mimic neural stiffness control through pressure sum.
It highlights challenges in accurately modeling static force across pressures.
Further research needed for precise inverse actuator modeling.
Abstract
The Festo air muscle is today one of the most known commercial version of the so-called McKibben pneumatic artificial muscle. A major advantage of handmade McKibben muscles, as well as its commercial versions, lies in the possibility it offers of realizing antagonist muscle actuator on the model of the biceps-triceps system. If pressures are independently controlled in each artificial muscle, it is then possible to define a position-stiffness control of the antagonist actuator by analogy with natural neural control of antagonist skeletal muscles. Such a control however requires a knowledge model of the actuator making possible a stiffness estimation provided by control pressures, while position closed-loop control is facilitated by a feedforward model of this highly nonlinear actuation device. We discuss this issue in the particular case of the antagonist Festo air muscle actuator, and…
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Taxonomy
TopicsMuscle activation and electromyography studies · Prosthetics and Rehabilitation Robotics · Motor Control and Adaptation
