Search

We are considering the problem of modelling and observer design for a fleet of Plug-in Electric vehicles (PEVs) in a public station connected to a smart grid. Both charging (Grid-to-Vehicle (G2V)) and discharging (Vehicle-to-Grid (V2G)) are focused on. We first develop models of fleet dynamics in both modes. The models are 1D hyperbolic-type partial differential equations (PDEs), and their well-posed is analysed using the method of characteristics, while their discretisation is performed using the θ-scheme. Then, we design observers that provide online estimates of the state variable along the PDE domain. The design and the analysis of observers are performed using Volterra integral backstepping transformation. Finally, numerical simulations are provided that confirm the theoretical convergence results in G2V and V2G modes. [ABSTRACT FROM AUTHOR]

This paper deals with the identification of Hammerstein–Wiener models. The novelty lies in the fact that the front nonlinear block is allowed to be the memory of hysteresis type. The latter is any rate-independent hysteresis operator, that is characterised by closed limit cycles when excited by periodic input signals called 'loading-unloading' (or 'increasing-decreasing'). Designing appropriate 'loading-unloading' periodic inputs will prove to be crucial in the proposed identification method. By using such signals, the intermediary linear subsystem is made transparent and the hysteresis front block is equivalent to two static nonlinearities (i.e. the lateral borders of the limit cycle). Then, the input and output signals turn out to be related by two composed functions. A decomposition algorithm is then resorted to extract the back nonlinearity and both lateral borders of the front hysteresis operator. Finally, the impulse response of the intermediary linear subsystem is determined using a least-squares like estimator. The quality of the estimated model is checked using a theoretical analysis and simulation results. [ABSTRACT FROM AUTHOR]

Existing observers that accommodate distributed-delays are mainly designed for linear systems with continuous-time output measurements. In this paper, we seek sampled-measurements-based observers for nonlinear systems with strict-feedback globally Lipschitz dynamics and distributed-delay. Invoking the high-gain principle, we design two observers operating with sampled output measurements. In both, the delay effect is compensated for by using distributed output-predictors. The first observer involves an intersample predictor and its exponential convergence is established using a quadratic Lyapunov function. The second observer involves a zero-order-hold predictor and the exponential convergence is proved using a Lyapunov–Krasovskii functional. To the author's knowledge, it is the first time that an exponentially convergent observer is developed for nonlinear systems with output distributed-delay and sampled output measurements. [ABSTRACT FROM AUTHOR]

We are considering the problem of observer design for a general class of state-affine systems in presence of output delay and sampling. Two novelties characterize the considered class of systems: (i) the state equation is subject to output injection involving future output values (not accessible to measurement); (ii) the injected output values come in the state equation not only through a driving term but also through the state matrix. These novel characteristics entail the loss of the system state-affine nature and lead to a new observer design problem never investigated so far. The solution we develop in this paper is a sampled-data based observer of Kalman-like type, augmented with inter-sample predictors and signal saturations. Using both Lyapunov and small-gain arguments, we show that the observer is exponentially convergent for sufficiently small sampling interval and delay, provided an observability condition is satisfied. [ABSTRACT FROM AUTHOR]