Stefan Kersting

  • Citations Per Year
Learn More
In this paper, we enhance a recently proposed method for adaptive identification of piecewise affine systems by the use of concurrent learning. It is shown that the concurrent use of recorded and instantaneous data leads to exponential convergence of all subsystem parameters under verifiable conditions on the recorded data. A key advantage of the proposed(More)
This paper presents a novel procedure for the online identification of continuous-time switched linear systems. The proposed procedure is an extension of the well studied series-parallel parameter identifiers in adaptive control. With the proposed results, it is possible to identify the submodels of a switched system online, independent of the nature of the(More)
The focus of this paper is on similarity based controller selection, which is thought of as an essential component of cognitive control. The strength of cognitive systems lies in the ability to compare the current situation with previously experienced situations. This allows for an efficient reuse of previously successful solutions. Transferring this(More)
This paper is concerned with erroneous history stack elements in concurrent learning. Concurrent learning-based update laws make concurrent use of current measurements and recorded data. This replaces persistence of excitation by a less restrictive linear independence of the recorded data. However, erroneous or outdated data prevents convergence to the true(More)
This article proposes direct and indirect model reference adaptive control strategies for multivariable piecewise affine systems, which constitute a popular tool to model hybrid systems and approximate nonlinear systems. A chosen reference model, which can be linear or also piecewise affine, describes the desired closed-loop system behavior that is to be(More)
Usually the identification of piecewise affine systems consists of two steps. First, subsystem parameters are identified before the state space partition is reconstructed. For the later step, it is common to label the states of a recorded trajectory and separate differently labeled states with the help of linear support vector machines. In this paper, we(More)
  • 1