Total sliding-mode controller for PM synchronous servo motor driveusing recurrent fuzzy neural network
Rong-Jong Wai
Industrial Electronics, IEEE Transactions on
Volume 48, Issue 5, Oct 2001 Page(s):926 - 944
Digital Object Identifier 10.1109/41.954557
Summary:In this paper, the dynamic responses of a
recurrent-fuzzy-neural-network (RFNN) sliding-mode-controlled
permanent-magnet (PM) synchronous servo motor are described. First, a
newly designed total sliding-mode control system, which is insensitive
to uncertainties, including parameter variations and external
disturbance in the whole control process, is introduced. The total
sliding-mode control comprises the baseline model design and the curbing
controller design. In the baseline model design, a computed torque
controller is designed to cancel the nonlinearity of the nominal plant.
In the curbing controller design, an additional controller is designed
using a new sliding surface to ensure the sliding motion through the
entire state trajectory. Therefore, in the total sliding-mode control
system, the controlled system has a total sliding motion without a
reaching phase. Then, to overcome the two main problems with
sliding-mode control, i.e., the assumption of known uncertainty bounds
and the chattering phenomena in the control effort, an RFNN sliding-mode
control system is investigated to control the PM synchronous servo
motor. In the RFNN sliding-mode control system, an RFNN bound observer
is utilized to adjust the uncertainty bounds in real time. To guarantee
the convergence of tracking error, analytical methods based on a
discrete-type Lyapunov function are proposed to determine the varied
learning rates of the RFNN. Simulated and experimental results due to
periodic step and sinusoidal commands show that the dynamic behaviors of
the proposed control systems are robust with regard to uncertainties
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