Citation:
Liu S, Whidborne JF, Chumalee S. (2021) Disturbance observer enhanced neural network LPV control for a blended-wing-body large aircraft. IEEE Transactions on Aerospace and Electronic Systems, Volume 57, Number 5, October 2021, pp. 2689-2703
Abstract:
The problem of trajectory tracking control for a
Blended-Wing-Body (BWB) large aircraft with model
parameter uncertainties and unknown disturbances is
considered. A Linear Parameter-Varying (LPV) model is
derived from the nonlinear dynamics of the BWB aircraft from
which a robust linear parameter-varying controller is designed
to track a desired trajectory. Using a Single Quadratic
Lyapunov Function (SQLF) and an infinite number of linear
matrix inequalities to be evaluated at all vertices, a pair of
positive definite symmetric matrix solutions is determined via
Lyapunov stability theory and linear matrix inequality
technique. Furthermore, a disturbance-observer is designed to
process the unknown disturbances. Considering the plant exists
some model errors except for disturbances, a Radial Basis
Function Neural Network (RBFNN) approximation is
embedded into the SQLF LPV controller to improve tracking
performances, and a composite disturbance-observer based
Neural Network Single Quadratic Lyapunov Function
(NNSQLF) controller can realize desired trajectory tracking of
the linear parameter-varying system through regulating
performance weighting functions. The closed-loop system of
trajectory tracking control is proved to be asymptotically stable
by using Lyapunov theory. Simulation results of forward flight
speed and altitude tracking control of the BWB aircraft show
that the proposed disturbance-observer based NNSQLF control
can robustly stabilize the LPV system and precisely track the
desired trajectory by comparing with conventional SQLF
control and Parameter-Dependent Lyapunov Functions (PDLF)
control, even in unknown exterior disturbances and model
uncertainties.