Stabilization of sampled-data nonlinear systems via backstepping on their Euler approximate model

Control of a Hyperchaotic System Via Generalized Backstepping Method

 This paper investigates on control and stabilization of a new hyperchaotic system. The hyperchaotic system is stabilized using a new technique which called Generalized Backstepping Method (GBM). Because of its similarity to Backstepping approach, this method is called GBM. But, this method is more applicable in comparison with conventional Backstepping.  Backstepping method is used only for sy...

On Stability of Sets for Sampled-Data Nonlinear Inclusions via Their Approximate Discrete-Time Models and Summability Criteria

This paper consists of two main parts. In the first part, we provide a framework for stabilization of arbitrary (not necessarily compact) closed sets for sampled-data nonlinear differential inclusions via their approximate discrete-time models. We generalize [19, Theorem 1] in several different directions: we consider stabilization of arbitrary closed sets, plants described as sampled-data diff...

Robust backstepping for the Euler approximate model of sampled-data strict-feedback systems

Stabilization of sampled–data nonlinear systems via their approximate models: an optimization based approach

We present results on numerical regulator design for sampled-data nonlinear plants via their approximate discrete-time plant models. The regulator design is based on an approximate discrete-time plant model and is carried out either via an infinite horizon optimization problem or via a finite horizon with terminal cost optimization problem. We focus on the case when the sampling period T and th...

Optimization-Based Stabilization of Sampled-Data Nonlinear Systems via Their Approximate Discrete-Time Models

We present results on numerical regulator design for sampled-data nonlinear plants via their approximate discrete-time plant models. The regulator design is based on an approximate discrete-time plant model and is carried out either via an infinite horizon optimization problem or via a finite horizon with terminal cost optimization problem. In both cases we discuss situations when the sampling ...