Abstract:
This paper proposes a novel integrated approach
for the identification and control of Hammerstein systems to
achieve desired heart rate tracking performance for an automated
treadmill system. The pseudo-random binary sequence
input is employed to decouple the identification of dynamic
linear part from static nonlinearity. The powerful ε-insensitivity
Support Vector Regression is adopted to obtain sparse representations
of the inversion of static nonlinearity in order to obtain
an approximated linear model of the Hammerstein system. An
H∞ controller is designed for the approximated linear model
to achieve robust tracking performance. This new approach
is applied to the design of a computer-controlled treadmill
system for the regulation of heart rate during treadmill exercise.
Minimizing deviations of heart rate from a preset profile
is achieved by controlling the speed of the treadmill. Both
conventional Proportional- Integral- Derivative (PID) control
and the proposed approaches have been employed for the
controller design. The proposed algorithm achieves much better
heart rate tracking performance.
