In this paper, a robust output feedback tracking control scheme for motion control of uncertain robot manipulators without joint velocity measurement based on a second‐order sliding mode (SOSM) observer is presented. Two second‐order sliding mode observers with finite time convergence are developed for velocity estimation and uncertainty identification, respectiv...

Abstract The inverse problem analysis method provides an effective way for the structural parameter identification. However, uncertainties wildly exist in practical engineering problems. Due to coupling of multi-source measured responses and modeling parameters, traditional under deterministic framework faces challenges solving mechanism computing cost. In this paper, uncertain based on convex ...

A full-scale seven-story reinforced concrete shear wall building structure was tested on the UCSD-NEES shake table in the period October 2005 January 2006. The shake table tests were designed so as to damage the building progressively through several historical seismic motions reproduced on the shake table. A sensitivitybased finite element (FE) model updating method was used to identify damage...

In this paper, dynamic multilayer neural networks are used for nonlinear system on-line identification. Passivity approach is applied to access several stability properties of the neuro identifier. The conditions for passivity, stability, asymptotic stability and inputto-state stability are established. We conclude that the commonly-used backpropagation algorithm with a modification term which ...

Bayesian system identification is a theoretically well-founded and currently emerging area. We describe and evaluate two recent state-of-the-art sample-based methods for Bayesian parameter inference from the statistics literature, particle Metropolis-Hastings (PMH) and SMC, and apply them to a non-trivial real world system identification problem with large uncertainty present. We discuss their ...

In this paper, dynamic neural networks are used for engine model at idle speed on-line identification. Passivity approach is applied to access several stability properties of the neuro identifier. The conditions for passivity, stability, asymptotic stability and input-to-state stability are established. We conclude that the commonly-used backpropagation algorithm with a modification term which ...

In this paper, we define a measure of robustness for a set of parameterized transfer functions as delivered by classical prediction error identification and that contains the true system at a prescribed probability level. This measure of robustness is the worst case Vinnicombe distance between the model and the plants in the uncertainty region. We show how it can be computed exactly using LMI-b...

If a matrix effect is detected during the validation stage of an analytical method, the standard addition methodology must be applied. To reach a good estimation of the uncertainty associated with the determination, an adequate identification and evaluation of each uncertainty source should be done. As an example to illustrate how to calculate the uncertainty in this case, the simultaneous dete...

In this paper a robust identification strategy for Wiener like models constituted by a linear dynamic block in series with a Piecewise Linear (PWL) function as the nonlinear static gain is presented. The proposed realization allows straightforward characterization of the static gain uncertainty. A robust Model Predictive Control (MPC) algorithm, using the presented modeling strategy, is develop...

Johannesburg water is responsible for the supply and monitoring of drinking water supplied to the greater Johannesburg area. Part of this monitoring programme involves the determination of Trihalomethane (THM) levels and the comparison thereof against the limits stipulated in SANS 241. This paper demonstrates the approach used to determine the uncertainty of the analytical method associated wit...