In this paper, we establish a unified framework for subspace identification (SID) of linear parameter-varying (LPV) systems to estimate LPV state–space (SS) models in innovation form. This enables us derive novel SID schemes that are extensions existing time-invariant (LTI) methods. More specifically, the open-loop, closed-loop, and predictor-based data-equations (input–output surrogate forms S...

In this paper we study the problem of developing a control law which can force the output of a linear time-varying plant to track the output of a stable linear time-invariant reference model. We first show that the standard model reference controller, used for linear timeinvariant plants, cannot guarantee zero tracking error in general when the plant is time-varying. We then propose a new model...

The functional coefficient partially linear regression model is a useful generalization of the nonparametric model, partial linear model, and varying coefficient model. In this paper, the local linear technique and the L1 method are employed to estimate all the functions in the functional coefficient partially linear regression model. The asymptotic properties of the proposed estimators are stu...

In this contribution we investigate Model Order Reduction (MOR) for linear systems with time-varying parameters p(t). Such systems arise, for instance, in structural dynamics and multibody simulations when the position of an acting force on the physical structure varies with time. This behaviour is found in many industrial applications, e.g. in meshing gears, milling processes, bridge cranes or...

This article presents parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modi ed for polytopic models. The modi ed prediction error method is applied to an LPV aircraft system whose varying parameter is the ight velocity and model parameters are the stability and control deriv...

We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium...