A control system design procedure is proposed for a widely applicable class of discrete-time, non-linear systems in which the system nonlinearities are incorporated into a linear model structure in the form of State-Dependent Parameter (SDP) functions.
The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns.
This paper presents a fundamental design trade-off applicable to a class of linear positive systems.
In this work, we propose the use of a weighted variable gain super-twisting observer (WVGSTO), applicable to a class of second-order nonlinear systems that include a measurable weight on the unmeasured variable and the possibility of bounding the perturbations with measurable functions.
As such, it holds promise to be generally applicable to a wider class of application domains, specifically, Machine Learning.
During the past years, considerable effort has been put into developing, scheduling, and load balancing techniques and models for the processing of tasks in parallel and distributed systems.This chapter discusses existing scheduling model called the divisible task model to adapt it to cluster architectures and make it applicable to a broader class of applications.
This paper proposes a new sliding mode observer for fault reconstruction, applicable for a class of linear parameter varying (LPV) systems.
The present study considers a typical case of an esterification reaction with azeotropic mixtures, and the analysis is applicable to a class of reactions satisfying the criteria mentioned.
To underscore the generality of the proposed variation of I&I, we show that it is also applicable to a class of n-dimensional systems that contain, as a particular case, the cart and pendulum system.
The results are shown to be applicable to a class of globally Lipschitz systems.
It is applicable for a class of design and covariance matrices which involve Kronecker products.
