This paper concerns the recursive identification of nonlinear discrete-time systems for which the original equations of motion are not known. Since the true model structure is not available, we replace it with a generic nonlinear model. This generic model discretizes the state space into a finite grid and associates a set of velocity vectors to the nodes of the grid. The velocity vectors are then interpolated to define a vector field on the complete state space. The proposed method follows a Bayesian framework where the identified velocity vectors are selected by the maximum a posteriori (MAP) criterion. The resulting algorithms allow a recursive update of the velocity vectors as new data is obtained. Simulation examples using the recursive algorithm are presented.

 In this paper different strategies to model Ionic Polymer-Polymer Composite (IP2C), used as actuator, are compared. Starting from some previous results regarding the ionic polymer metal composites (IPMCs) modeling, a linear gray-box model has been determined for an IP2C actuator. Moreover linear and nonlinear black-box models have been identified from experimental data. A comparison among developed models has been performed in order to determine the model that better describes the actuator behaviour.

 This paper aims at obtaining a dynamic model as generic as possible of a parafoil/payload system. Modeling steps including the consideration of the added masses are detailed such that the reader can easily meet the result. Flight simulation and analysis tools are also presented with a user-friendly 3D visualization.

 This paper describes the experimental identification of the parameters of the Twin Rotor Mimo System (TRMS) non-linear model using data collected from the real lab set-up. From this non-linear model, a quasi-linear parameter varying (quasi-LPV) model has also been derived using a state transformation. This quasi-LPV model is approximated with a polytopic model using the bounding box approach. Such a model can later be used for control design. The model parameters have been calibrated by means of non-linear least-squares identification approach. Once the calibrated non-linear model has been obtained, a simulator has been built and validated against real data showing satisfactory results when compared to real data.

 This paper presents a procedure that generates control lists for electricity provider companies to reveal fraudulent customers with the highest possible efficiency. Based on the database of Customer Registration System, essential features are selected and other secondary (derived) properties are introduced to build up an abstract space for the algorithm of control list generation. Control list generation algorithm uses training data from the Customer Registration System to set up a fuzzy system with subtractive clustering. The algorithm has been validated at Hungarian Electricity Provider ELMŰ-MSZ Company Group.

 Even though a modeling of a chemical tank is, in our case, in principle the modeling of a heat exchanger and belongs to the classical tasks, there are always some phenomena in practice, which are either difficult or impossible to include into the model. This especially holds in case of the old devices, where these parasitic events can have quite a large effect and can degrade both the model and the control strategy. The chemical tank being dealt within this paper suffers from many unpleasant phenomena, making it hard to come up with a proper identification method and model. In this paper, several modeling and identification approaches applied to the industrial type of shell and tube heat exchangers are presented. The goal is to show that less complex model serves the purpose better than a complex model and is also suitable for model based control. The control of a heat exchanger has been treated in many papers using some of the classical control concepts. In contrary, we propose a predictive control scheme, which compared to previous control approach have saved 25% of energy.