text: New results related to the control theory of nonlinear systems were achieved on problems associated with robust control design based on Linear Parameter Varying (LPV) and quasi-Linear Parameter Varying (qLPV) models. Conventional convex LMI/LTI design approaches are in general not applicable to LPV and qLPV problems, or otherwise yield very conservative solutions. Therefore, traditional methods – based on the separation theory and IQC (Integral Quadratic Constraints) techniques – for the solutions of linear robust control problems were put under revision. The results obtained highlighted a common geometric background behind the well-known robust control design approaches that provide a suitable theoretical foundation to the design and analysis of controllers. The geometric aspects of the Klein approach were successfully applied for the analysis of robust control problems. The representation of points in Klein geometry, for instance, can be corresponded to the stabilizable systems, while the Mobius transformations are of a close similarity to the movements defining the geometry. It was shown that the transformations of certain hyperbolic spaces provide a common background to the treatment of robust control problems by explaining the relations of various well known approaches. These new observations lead to the solutions of various, earlier not solvable problems.
  New methods have been elaborated for the synthesis and analysis of practical fault tolerant control systems, using the approaches of fault detection and null space-based structural reconfiguration. Methods were developed that guarantee the fulfilment of the quality requirements using switching-based reconfiguration strategies and qLPV models. By means of the new geometric achievements (see above), new methods could be synthesized for sensor reconfiguration algorithms that preserve stability during reconfiguration actions. The results were applied to a variety of complex control problems in ground and aerial vehicle dynamics.
  Significant research efforts were made with respect to the efficient modelling of flexible engineering objects such as e.g. aircraft wings by means of the application of systematic mathematical model reduction processes. New results were obtained for the reduction of many hundred-dimension LPV models using Youla parametrization.
  In the area of signal processing and system identification: a new non-parametric system identification method was developed based on hyperbolic wavelet constructions. The method was inspired by approaches that perform system identification using rational orthogonal basis functions, and is capable of finding iteratively the poles of a system based on measurements in either time or frequency domain.
  The results obtained have been primarily used in the energy, transportation and vehicle industries. The institute has brought various industrial (e.g. Airbus, Bosch, Knorr-Bremse) and academic partners together in a number of Hungarian and European R&D projects, creating direct utilization of the theoretic achievements in the practice. Industrialized or nearindustry solutions were developed for the fault tolerant design of on-board vehicle control systems, the coordinated control of vehicle fleets, the intelligent driverless vehicle control, the sensor fusion methods, and the integrated control methods for electronic steering and braking.