This article proposes an adaptive neural output feedback control scheme
in combination with state and disturbance observers for uncertain
fractional-order nonlinear systems containing unknown external
disturbance, input saturation and immeasurable state. The radial basis
function neural network (RBFNN) approximation is used to estimate
unknown nonlinear function, and a state observer as well as a
fractional-order disturbance observer is developed simultaneously by
using the approximation output of the RBFNN to estimate immeasurable
states and unknown compounded disturbances, respectively. Then, a
fractional-order auxiliary system is constructed to compensate the
effects caused by the saturated input. In addition, by introducing a
dynamic surface control strategy, the tedious analytic computation of
time derivatives of virtual control laws in the conventional
backstepping method is avoided. The proposed method guarantees that the
boundness of all signals in the closed loop system and the tracking
errors converge to a small neighbourhood around the origin. Finally, two
examples are provided to verify the effectiveness of the proposed
control method.
Adaptive Fractional Fuzzy Sliding Mode Control for Multivariable Nonlinear Systems
