Towards a MATLAB Toolbox to compute backstepping kernels using the power series method

TL;DR

This paper advances the computation of backstepping kernels by developing a MATLAB toolbox using the power series method, improving efficiency, convergence, and handling singularities for control applications.

Contribution

It introduces a MATLAB toolbox for backstepping kernel computation, employing localized power series to improve convergence and manage singularities, building on previous work.

Findings

Significant speed improvements over symbolic software.

Effective handling of oscillatory behaviors and singularities.

Potential benefits for neural operator training.

Abstract

In this paper, we extend our previous work on the power series method for computing backstepping kernels. Our first contribution is the development of initial steps towards a MATLAB toolbox dedicated to backstepping kernel computation. This toolbox would exploit MATLAB's linear algebra and sparse matrix manipulation features for enhanced efficiency; our initial findings show considerable improvements in computational speed with respect to the use of symbolical software without loss of precision at high orders. Additionally, we tackle limitations observed in our earlier work, such as slow convergence (due to oscillatory behaviors) and non-converging series (due to loss of analiticity at some singular points). To overcome these challenges, we introduce a technique that mitigates this behaviour by computing the expansion at different points, denoted as localized power series. This approach effectively navigates around singularities, and can also accelerates convergence by using more local approximations. Basic examples are provided to demonstrate these enhancements. Although this research is still ongoing, the significant potential and simplicity of the method already establish the power series approach as a viable and versatile solution for solving backstepping kernel equations, benefiting both novel and experienced practitioners in the field. We anticipate that these developments will be particularly beneficial in training the recently introduced neural operators that approximate backstepping kernels and gains.