The structural control can provide the potential protection through passive and active control techniques. Structures with base isolations have been successfully implemented and proven effective in the vibration mitigation. To enhance the functionality of base isolations, a hybrid control system can be considered using a combination with active control devices. This research applies the hybrid ...

Subspace System Identification is by now an established methodology for experimental modelling. The basic theory is well understood and it is more or less a standard tool in industry. The two main research problems in subspace system identification that have been studied in the recent years are closed loop system identification and performance analysis. The aim of this contribution is quite dif...

Sampled-data control systems occasionally exhibit aliased resonance phenomena within the control bandwidth. The aim of this paper is to investigate the aspect of these aliased dynamics with application to a high performance industrial nanopositioning machine. This necessitates a full sampled-data control design approach, since these aliased dynamics endanger both the at-sample performance and t...

A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

In this paper, we propose a novel Takagi-SugenoKang type interval-valued neural fuzzy system with asymmetric fuzzy membership functions (called TIVNFS-A). In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. Based on the Lyapunov stability theorem, the TIVNFS-A system is trained by the back-propag...

We propose a model validation procedure that consists of a prediction error identi6cation experiment with a full order model. It delivers a parametric uncertainty ellipsoid and a corresponding set of parameterized transfer functions, which we call prediction error (PE) unceminty set. Such uncertainty set differs from the classical uncertainty descriptions used in robust control analysis and des...

The complexities of the dynamic processes and their control associated with biological and ecological systems offer many challenges for the control engineer. Over the past decades the application of dynamic modelling and control has aided understanding of their complexities. At the same time using such complex systems as test-beds for new control methods has highlighted their limitations (e.g. ...

This paper introduces a new robust-control-oriented system identification method, which consists of the following three steps: 1. High-order ARX model identification; 2. Loop shaping weighting functions design based on the high-order ARX model; 3. Control-oriented model reduction by minimizing the weighted L2-gap between the high-order ARX model and the low-order model. This method truly integr...