Enhanced compensation control of pneumatic muscle actuator with high-order modified dynamic model.

Dynamic behaviour of the pneumatic muscle actuator (PMA) is conventionally modelled as a pressure-based first-order equation under discrete loads, which cannot exactly describe its dynamic features. Considering PMA's nonlinear, time-varying and hysteresis characteristics, we propose a novel high-order modified dynamic model of PMA based on its physical properties and working principle, with coefficients being identified under external dynamic loads. To tackle PMA's nonlinear hysteresis problem in high-frequency movements, a global fast terminal sliding mode controller with the modified model-based radial basis function (RBF) neural network disturbance compensator (RBF-GFTSMC) is designed. Comparison experimental studies are carried on a designed PMA platform that can provide continuously changing loads. Results show that the RBF-GFTSMC has superior trajectory tracking performance and disturbance compensation capability under wide-ranged frequencies and external loads, which can be potentially used to achieve precise control of PMA-actuated robots.