Neural network fuzzy sliding mode control of pneumatic muscle actuators.

The pneumatic muscle actuator (PMA) is one of the most promising actuators especially for the applications that require greater proximity between the humans and the robots. Fast and precise control of PMA, however, is difficult to achieve due to the compressibility of the air and the elasticity of the PMA. In order to achieve accurate and consistent tracking performance of a one axis PMA actuated manipulator over considerably wide ranges of frequency and stroke, an intelligent adaptive control algorithm is developed in this paper. The adaptive learning is enabled by a neural network in which the control gains to a fuzzy sliding mode controller (FSMC) and an integrator are adjusted to minimize the tracking error. Experimental results show that the proposed control strategy achieves fast, accurate and consistent performance tracking sinusoidal reference trajectories up to 1 Hz in frequency and close to the extreme stroke of the PMA actuated manipulator with the compressed air regulated to 4 bar. Results also show that the proposed control strategy, with a more aggressive learning for the control gain to the FSMC, achieves satisfactory performance tracking a trapezoidal reference trajectory. [ABSTRACT FROM AUTHOR]