Implementation of Iterative Learning Control on a Pneumatic Actuator.
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Date
2022
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Abstract
Pneumatic systems play a pivotal role in many industrial applications, such as in
petrochemical industries, steel manufacturing, car manufacturing and food industries. Besides
industrial applications, pneumatic systems have also been used in many robotic systems.
Nevertheless, a pneumatic system contains different nonlinear and uncertain behaviour due to
gas compression, gas leakage, attenuation of the air in pipes and frictional forces in mechanical
parts, which increase the system’s dynamic orders. Therefore, modelling a pneumatic system
tends to be complicated and challenges the design of the controller for such a system. As a
result, employing an effective control mechanism to precisely control a pneumatic system for
achieving the required performance is essential.
A desirable controller for a pneumatic system should be capable of learning the dynamics of
the system and adjusting the control signal accordingly. In this study, a learning control scheme
to overcome the highlighted nonlinearity problems is suggested. Many industrial processes are
repetitive, and it is reasonable to make use of previously acquired data to improve a controller’s
convergence and robustness. An Iterative Learning Control (ILC) algorithm uses information
from previous repetitions to learn about the system’s dynamics. The ILC algorithm
characteristics are beneficial in real-time control given its short time requirements for
responding to input changes.
Cylinder-piston actuators are the most common pneumatic systems, which translate the air
pressure force into a linear mechanical motion. In industrial automation and robotics, linear
pneumatic actuators have a wide range of applications, from load positioning to pneumatic
muscles in robots. Therefore, the aim of this research is to study the performance of ILC
techniques in position control of the rod in a pneumatic position-cylinder system. Based on
theoretical analysis, the design of an ILC is discussed, showing that the controller can
satisfactorily overcome nonlinearities and uncertainties in the system without needing any prior
knowledge of the system’s model. The controller has been designed in such a way to even work
on non-iterative processes. The performance of the ILC-controlled system is compared with a
well-tuned PID controller, showing a faster and more accurate response.
Description
Masters Degree. University of KwaZulu-Natal, Durban.