Biologically plausible robust control with Neural Network weight reset for unmanned aircraft systems under impulsive disturbances

Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying stability analyses and mathematical proofs rely on unrealistic assumptions which limit not only the performance, but also the implementation of such controllers in real-world scenarios. In this work the limbic system inspired control (LISIC) framework is revisited, introducing three contributions that facilitate the implementation of this type of controller in real-time. First, an extension enabling the implementation of LISIC to the domain of SISO affine systems is proposed. Second, a strategy for resetting the controller’s Neural Network (NN) weights is developed, in such a way that now it is possible to deal with piece-wise smooth references and impulsive perturbations. And third, for the case when a nominal model of the system is available, a technique is proposed to compute a set of optimal NN reset weight values by solving a convex constrained optimization problem. Numerical simulations addressing the stabilization of an unmanned aircraft system via the robust LISIC demonstrate the advantages obtained when adopting the extension to SISO systems and the two NN weight reset strategies.

I. Introduction

The dynamics of unmanned aircraft systems (UASs) are generally represented using a nonlinear mathematical model described by a set of first order differential equations. For UAS control design purposes, feedback linearization is a well known technique which, under the implementation of an appropriate feedback controller, renders the input-output dynamics of a nonlinear plant linear. Once a linearizing controller has been constructed, desired output trajectories for the nonlinear plant can be tracked using a variety of linear control techniques. However, the calculation of a linearizing controller requires a precise knowledge of the nonlinear dynamic model of the system, which are usually not available or disclosed by the manufacturers of this kind of system.