Design of LPV-Based Sliding Mode Controller with Finite Time Convergence for a Morphing Aircraft

Abstract

This paper proposes a finite time convergence sliding mode control (FSMC) strategy based on linear parameter-varying (LPV) methodology for the stability control of a morphing aircraft subject to parameter uncertainties and external disturbances. Based on the Kane method, a longitudinal dynamic model of the morphing aircraft is built. Furthermore, the linearized LPV model of the aircraft in the wing transition process is obtained, whose scheduling parameters are wing sweep angle and wingspan. The FSMC scheme is developed into LPV systems by applying the previous results for linear time-invariant (LTI) systems. The sufficient condition in form of linear matrix inequality (LMI) constraints is derived for the existence of a reduced-order sliding mode, in which the dynamics can be ensured to keep robust stability and L2 gain performance. The tensor-product (TP) model transformation approach can be directly applied to solve infinite LMIs belonging to the polynomial parameter-dependent LPV system. Then, by the parameter-dependent Lyapunov function stability analysis, the synthesized FSMC is proved to drive the LPV system trajectories toward the predefined switching surface with a finite time arrival. Comparative simulation results in the nonlinear model demonstrate the robustness and effectiveness of this approach.

LPV-Based Self-Adaption Integral Sliding Mode Controller With $L_{2}$ Gain Performance for a Morphing Aircraft

“…A flight mechanics modeling method is proposed to solve the time-varying characteristics of the aerodynamic parameters, moment of inertia and centroid of a variant aircraft during the variant process, and flight simulation verification is carried out with the ''gull wing'' aircraft [6]. The Kane multibody modeling method is used to establish a nonlinear dynamic model including the variant structure of the morphing aircraft [7], [8].…”

“…Therefore, it is impossible to accurately express the dynamic characteristics of a morphing aircraft in which a plurality of deformation parameters exist. At the same time, the above literature does not consider the existence of model perturbation and interference, and later studies have shown that the H ∞ -type controller has a poor control effect under this condition [7].…”

“…However, all of those solutions are restricted to the single operational condition, and most of them are sliding mode observers for special LPV applications only [23], [24]. To further improve the problems in the above documents, a class of parameterdependent sliding mode controllers with finite time convergence is derived using LMI conditions, which guarantees the stability of a complex variant aircraft morphing process, and a detailed theoretical derivation is carried out in [7]. However, the single selection of its Lyapunov matrix leads to the sliding surface not achieving a true parametric dependence.…”

Neural network-based nonlinear dynamic inversion control of variable-span morphing aircraft

“…Consequently, the problem of morphing aircraft flight controller design has been addressed via various approaches that can capture the parameter-varying nature of the system, such as linear parameter-varying control, 12–17 adaptive control, 18–22 and sliding mode control. 16,17 However, no previous study has considered handling qualities criteria. The objective of this study is to develop a control system that can fully exploit the shape-changing ability while rigorously satisfying all the handling qualities requirements at the same time.…”

“…On the other hand, a gain scheduling (conventional and linear-parameter varying) approach, model-reference adaptive control scheme, and various nonlinear control techniques such as feedback linearization, sliding mode, and back-stepping control methods have been studied. Consequently, the problem of morphing aircraft flight controller design has been addressed via various approaches that can capture the parameter-varying nature of the system, such as linear parameter-varying control, [12][13][14][15][16][17] adaptive control, [18][19][20][21][22] and sliding mode control. 16,17 However, no previous study has considered handling qualities criteria.…”