Precision mechatronic systems are challenging to identify, since they typically have complex dynamics and multiple inputs and outputs. In addition, sampling effects, such as aliasing, increase the complexity of identification, and is for instance seen in vision-in-the-loop systems. In this talk, a new identification technique that utilizes local models is discussed, that enables fast and accurate identification of complex multi-input multi-output systems, including identification above the Nyquist frequency. Results confirm a significant improvement with respect to traditional frequency response function identification using noise excitation.
- Lectures hall 3