Incremental Nonlinear Dynamic Inversion Control of Pneumatic Actuators

Abstract

Pneumatic cylinders provide an environment-friendly actuation means by minimizing the leakage of any harmful industrial fluids, as occurs for hydraulic actuators. Thus, pneumatic actuators require less maintenance, compared to hydraulic actuators. Moreover, for a similar weight of hydraulic actuator, the cost of a pneumatic actuation system is less. However, pneumatic actuation has not been utilized widely for industrial applications due to its highly-nonlinear nature. The compressibility property of air, friction forces in the cylinder and the switching dynamics of air flow-rate through the valve are some of the causes for this non-linearity. Therefore, these characteristics can often make the implementation of a model-dependent controller for a pneumatic system difficult. Incremental nonlinear dynamic inversion (INDI) is a control approach which uses less plant-model information, and is thus inherently robust to mismatches in the known plant-model, and also to external disturbances. INDI has recently gained popularity, especially in the aerospace-control research community, but it has never been implemented for controlling a pneumatic system, which necessitates additional research. Therefore, developing an incremental nonlinear controller for a pneumatic system is the main focus of this research article which is accomplished by utilizing a cascaded-control approach, where the inner-loop INDI tracks a given force and the outer-loop NDI is for controlling the piston-position. Moreover, realistic sensor noises have been added in the simulation and the robustness of incremental approach is demonstrated with respect to a baseline PID controller. Besides this, the external load attached to the cylinder-piston is increased by five times and also made variable, in order to show the effectiveness of the incremental control approach. Furthermore, a first-order filter is used for attenuating the sensor noise and the pneumatic valve is simulated using a first-order model. Finally, a series of recommendations is discussed at the end, for future works.