Backstepping designs for the stabilisation of nonlinear sampled-data systems via approximate discrete-time model

Ustunturk, Ahmet
Kocaoglan, Erol
The problems of backstepping, adaptive backstepping and reduced order observer based output feedback control of sampled-data nonlinear systems in strict feedback form are considered. Controller design methods based on the Euler approximate model are presented for these problems. The controllers are designed to compensate the effects of the discrepancy between the Euler approximate model and exact discrete time model, parameter estimation error in adaptive control and observer error in output feedback control, which behave as disturbance. It is shown that the obtained controllers semi-globally practically asymptotically stabilise the plant model under standard assumptions. Then numerical examples are given to illustrate the design methods.


BODUR, M; SEZER, ME (Informa UK Limited, 1993-09-01)
An adaptive self-tuning control scheme is developed for end-point position control of flexible manipulators. The proposed scheme has three characteristics. First, it is based on a dynamic model of a flexible manipulator described in cartesian coordinates, which eliminates the burden and inaccuracy of translating a desired end-point trajectory to joint coordinates using inverse kinematic relations. Second, the effect of flexibility is included in the dynamic model by approximating flexible links with a numbe...
Exhaustive study on the commutativity of time-varying systems
KÖKSAL, MUHAMMET (Informa UK Limited, 1988-5)
This paper, which is a survey and a compact reference on the commutativity of time-varying systems, gives the complete set of necessary and sufficient commutativity conditions for systems of any order. Original results are derived on Euler's systems, and explicit commutativity conditions are presented for fourth-order systems, which have not yet appeared in the literature.
An improved method for inference of piecewise linear systems by detecting jumps using derivative estimation
Selcuk, A. M.; Öktem, Hüseyin Avni (Elsevier BV, 2009-08-01)
Inference of dynamical systems using piecewise linear models is a promising active research area. Most of the investigations in this field have been stimulated by the research in functional genomics. In this article we study the inference problem in piecewise linear systems. We propose first identifying the state transitions by detecting the jumps of the derivative estimates, then finding the guard conditions of the state transitions (thresholds) from the values of the state variables at the state transitio...
A state prediction scheme for discrete time nonlinear dynamic systems
Demirbaş, Kerim (Informa UK Limited, 2007-01-01)
A state prediction scheme is proposed for discrete time nonlinear dynamic systems with non-Gaussian disturbance and observation noises. This scheme is based upon quantization, multiple hypothesis testing, and dynamic programming. Dynamic models of the proposed scheme are as general as dynamic models of particle predictors, whereas the nonlinear models of the extended Kalman (EK) predictor are linear with respect to the disturbance and observation noises. The performance of the proposed scheme is compared wi...
Analysis of single Gaussian approximation of Gaussian mixtures in Bayesian filtering applied to mixed multiple-model estimation
Orguner, Umut (Informa UK Limited, 2007-01-01)
This paper examines the effect of the moment-matched single Gaussian approximation, which is made in various multiple-model filtering applications to approximate a Gaussian mixture, on the Bayesian filter performance. The estimation error caused by the approximation is analysed for both the prediction and the measurement updates of a Bayesian filter. An approximate formula is found for the covariance of the error caused by the approximation for a general Gaussian mixture with arbitrary components. The calcu...
Citation Formats
A. Ustunturk and E. Kocaoglan, “Backstepping designs for the stabilisation of nonlinear sampled-data systems via approximate discrete-time model,” INTERNATIONAL JOURNAL OF CONTROL, pp. 893–911, 2013, Accessed: 00, 2020. [Online]. Available: