Neuroadaptive output-feedback trajectory tracking control for a stratospheric airship with prescribed performance

Abstract

<jats:title>ABSTRACT</jats:title> <jats:p>In this article, we investigate the horizontal trajectory tracking problem for an underactuated stratospheric airship subject to nonvanishing external disturbances and model uncertainties. By transforming the tracking errors into new virtual error variables, we can specify the transient and steady-state tracking performance of the resulting nonlinear system quantitatively, which means that under the proposed control scheme, the tracking errors will converge to prescribed residual sets around the origin before a preselected finite time with decay rates no less than a preassignable value. To address unknown items, minimal learning parameter (MLP) techniques for neural networks (NNs) approximation are employed, which efficaciously relax the computational burden, enhance the robustness against dynamics uncertainties and provide an improved property for disturbances rejection. A finite-time convergent observer (FTCO) is incorporated into the control framework to realise output-feedback control, ensuring that estimation errors are bounded during operation and approach zero within a finite time. Stability analysis proves that all the closed-loop signals are uniformly bounded. The effectiveness and advantages of the proposed control strategy are verified by simulation results.</jats:p>

Finite-time velocity-free trajectory tracking control for a stratospheric airship with preassigned accuracy

“…This dramatically accelerates the development of the long-dwell stratospheric airship [4], which is a typical lighter-than-air aircraft that plays various roles from telecommunication to space-like observation outpost, similar to satellites [5][6][7][8]. To accomplish diverse mission objectives, driving the airship to reach and follow a time parameterised reference route, also termed as trajectory tracking control, is the most fundamental flight control task [9][10][11]. However, high nonlinearities, strong couplings, modeling inaccuracies, and unpredictable disturbances render the trajectory tracking control design quite intractable.…”

“…However, no effective modification technique was provided to eliminate control chattering, thus yielding its implementation impossible. In other works [6,10,[15][16][17], the unmodeled dynamics and external disturbances were identified and compensated by neural networks (NNs) or fuzzy logic systems (FLSs). However, the employment of NNs and FLSs will inevitably make the controller computationally expensive due to their inherent attributes.…”

“…This poses new challenges as the lateral underactuation imposes a non-integrable restriction on the acceleration of the airship, and therefore it has become an important topic of research [3,[19][20][21][22]. Toward underactuated airships and other types of underactuated vehicles, several controllers have been proposed, both of which have great reference values for us, including the waypoint navigation method [23], the transverse function control [20], and the line of sight (LOS) approach [6,10,17,21]. The controller grounded on the waypoint navigation method [23] demands pre-planning an optimal course that aligns with the direction of the wind, which may be suitable for hovering control rather than trajectory tracking control.…”

“…Generally, the error-constrained problem is unavoidably linked to rather complex nonlinear mappings. Some prime examples can be found in the works of Jia et al [21] and Wu et al [10], both of which introduced barrier functions and error-dependent transformations to meet such a restrictive condition.…”

“…Nevertheless, own to the coupled nonlinearities in the kinematic equation of airships, extending these finite-time controllers to the trajectory tracking control design for airships is nontrivial, especially for underactuated ones. For fully actuated airships, several finite-time trajectory tracking or path following control algorithms were constructed [10,17], where the hard computation of time derivatives of virtual control laws was obviated through filter tools. Though the filter can avoid repeated differentiation, it still structurally increases the complexity of control systems.…”