An algorithm for the integration of meshes derived from range data is presented. All of the triangles from either mesh which have sampled the same surface patch of the object are identified as co-measurements and only the most confidently acquired one is kept. Co-measurement identification is based on the position and orientation of the range scanner when each triangle was sampled. Once the red...

Prediction-based Iterative Learning Control (PILC) is proposed in this paper for a class of time varying nonlinear uncertain systems. Convergence of PILC is analyzed and the uniform boundedness of tracking error is obtained in the presence of uncertainty and disturbances. It is shown that the learning algorithm not only guarantees the robustness, but also improves the learning rate despite the ...

Standard (black-box) regression models may not necessarily suffice for accurate identification and prediction of thermal dynamics in buildings. This is particularly apparent when either the flow rate or the inlet temperature of the thermal medium varies significantly with time. To this end, this paper analytically derives, using physical insight, and investigates linear regression models with n...

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...

This paper discusses the Hammerstein model identification using a blind approach. By fast sampling at the output, it is shown that identification of the linear part can be achieved based only on the output measurements that makes the Hammerstein model identification possible without knowing the structure of the nonlinearity and the internal variables.

Structural system identification using recursive methods has been a research direction of increasing interest in recent decades. The two prominent methods, including the Extended Kalman Filter (EKF) and the Particle Filter (PF), also known as the Sequential Monte Carlo (SMC), are advantageous in this field. In this study, the system identification of a shake table test of a 4-story steel struct...