Fuzzy logic based PWM control and neural controlled-variable estimation of pneumatic artificial muscle actuators

There is increasing research to explore both efficient and cost-effective control of pneumatic artificial muscle (PAM) actuators against inherent nonlinear behaviors of the actuators themselves. This paper presents a fuzzy logic based Pulse-Width-Modulation (PWM) control of PAM actuators together with controlled-variable estimation of a neural network. The PAM actuator consists of two main non-linear elements: a PAM and an on/off control valve unit. Dynamic modeling of the PAM actuator is carried out so as to represent a real PAM actuator in simulation of the dynamic behaviors for gaining knowledge in the controller design. The proportional-type fuzzy control law based on a minimum-time control design is proposed to determine the mass flow rate of compressed air in manipulating the controlled variables of the PAM actuators, such as position and force. In circumstances, when the controlled variables are inaccessible, a neural network model is proposed to estimate those variables instead of using direct measurement. The class of PAM actuators available in industry is used as a practical example to show the effectiveness of the proposed methodology in real working conditions. The concept of the proposed knowledge-based control system, which can emulate the reasoning procedures in this work, can be generalized to systematically implement other PAM actuators in real-time control.