IQClab

Dear visitor, supporter and/or user of IQClab. We have news!

IQClab is now also commercially available (free of charge) and can be downloaded from Github. Click here for the terms and conditions as well as a link to Github where the tools can be downloaded.

In addition, we have added a new function to the toolbox for performing parametric sensitivity analyses by means of various methods. Please follow this link for a description of the tools together with a user guide. Finally, a demonstrating example can be found here.

Dear Visitor, supporter, and/or user of IQClab. We have now included an algorithm for the synthesis of robust estimators. Please follow this link for the user guide and this link for a demo. A new release of IQClab is now available.

Dear all, we are pleased to announce that IQClab, next to continuous-time robustness analysis, now fully supports performing robustness analyses for discrete-time uncertain systems. This was already possible for most uncertainty classes with the exception of the class of diagonally repeated sector-bounded and slope restricted nonlinearities. Therefore, we have now implemented a discrete-time (asymptotically) tight parameterization of the full-block Zames-Falb multiplier as reported in [23]. A new release of IQClab is now available.

Dear visitor, supporter, and/or user of IQClab. This is to let you know that IQClab is now capable of performing various types of invariant/regional analyses. Please follow this link for further details. A new release of IQClab is now available.

Dear visitor, welcome to IQClab. We are now live!

See how to get a free license here.

This portal gives you access to state of the art integral quadratic constraint (IQC) based tools for performing robustness analyses and designing control algorithms for a large class of uncertain and linear parameter varying (LPV) systems. In addition, IQClab consists of an extensive set of auxiliary tools and functions for performing model reduction, implementing control switching schemes, generating performance weighting functions, among others. Not only are the tools easy to use, but they also have a modular build and can be applied in combination with different parsers and solvers. This allows developers to seamlessly include new linear matrix inequality (LMI) and IQC based algorithms as well as other extensions.