Abstract:
This paper proposes a novel integrated approach for
the identification and control of Hammerstein systems to achieve
desired heart rate profile tracking performance for an automated
treadmill system. For the identification of Hammerstein systems,
the pseudorandom binary sequence input is employed to decouple
the identification of dynamic linear part from input nonlinearity.
The powerful -insensitivity support vector regression method is
adopted to obtain sparse representations of the inverse of static
nonlinearity in order to obtain an approximate linear model of
the Hammerstein system. An controller is designed for the
approximated linear model to achieve robust tracking performance.
This new approach is successfully 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.
