This communique proposes a multivariable super-twisting sliding mode structure which represents an extension of the wellknown single input case. A Lyapunov approach is used to show finite time stability for the system in the presence of a class of uncertainty. This structure is used to create a sliding mode observer to detect and isolate faults for a satellite system.

No abstract

No abstract

Insects are conventionally modelled as controlling flight by varying a few summary kinematic parameters that are defined on a per-wingbeat basis, such as the stroke amplitude, mean stroke angle and mean wing pitch angle. Nevertheless, as insects have tens of flight muscles and vary their kinematics continuously, the true dimension of their control input space is likely to be much higher. Here, we present a compact description of the deforming wing kinematics of 36 manoeuvring Eristalis hoverflies, applying functional principal components analysis to Fourier series fits of the wingtip position and wing twist measured over 26 541 wingbeats. This analysis offers a high degree of data reduction, in addition to insight into the natural kinematic couplings. We used statistical resampling techniques to verify that the principal components (PCs) were repeatable features of the data, and analysed their coefficient vectors to provide insight into the form of these natural couplings. Conceptually, the dominant PCs provide a natural set of control input variables that span the control input subspace utilized by this species, but they can also be thought of as output states of the flight motor. This functional description of the wing kinematics is appropriate to modelling insect flight as a form of limit cycle control.

No abstract

No abstract

In this paper a comparison between a first order sliding mode and a super-twisting based observer is made for a nonlinear benchmark satellite system subject to uncertainties and sensor noise. Fully open and fully closed thruster faults are applied to both the rigid body and flexible mode models. The performance of a first order sliding mode observer based on the unit vector approach, and a second order super-twisting observer are compared using Monte Carlo simulations.

Insects are conventionally modelled as controlling flight by varying a few summary kinematic parameters that are defined on a per-wingbeat basis, such as the stroke amplitude, mean stroke angle, and mean wing pitch angle. Nevertheless, as insects have tens of flight muscles and vary their kinematics continuously, the true dimension of their control input subspace is likely to be much higher. Here we present a compact description of the deforming wing kinematics of 36 manoeuvring Eristalis hoverflies, applying functional principal components analysis to Fourier series fits of the wingtip position and wing twist measured over 26,541 wingbeats. This analysis offers a high degree of data reduction, in addition to insight into the natural kinematic couplings. We used statistical resampling techniques to verify that the principal components were repeatable features of the data, and analysed their coefficient vectors to provide insight into the form of these natural couplings. Conceptually, the dominant principal components provide a natural set of control input variables that span the control input subspace of this species, but they can also be thought of as output states of the flight motor. This functional description of the wing kinematics is appropriate to modelling insect flight as a form of limit cycle control.