Abstract This paper presents a method to identify and
control electro-pneumatic servo drives in a real-time
environment. Acquiring the system’s transfer function
accurately can be difficult for nonlinear systems. This
causes a great difficulty in servo-pneumatic system modeling
and control. In order to avoid the complexity associated
with nonlinear system modeling, a mixed-reality environment
(MRE) is employed to identify the transfer function of the
system using a recursive least squares (RLS) algorithm based
on the auto-regressive moving-average (ARMA) model. Online
system identification can be conducted effectively and
efficiently using the proposed method. The advantages of the
proposed method include high accuracy in the identified
system, low cost, and time reduction in tuning the controller
parameters. Furthermore, the proposed method allows for online
system control using different control schemes. The
results obtained from the on-line experimental measured data
are used to determine a discrete transfer function of the
system. The best performance results are obtained using a
fourth-order model with one-step prediction.
Keywords On-line identification .
Auto-regressive moving-average . Pneumatic servo drive .
Mixed-reality environment