In cyberphysical systems, where compositionality of design is an important requirement, passivity and dissipativity based design methods have shown a lot of promise. Although these concepts are classical, their application to cyberphysical systems poses new and interesting challenges. The aim of this paper is to summarize some of the ongoing work in this area by the authors. I. INTRODUCTION Cyberphysical systems: Cyberphysical systems (CPS) have now emerged as a major research avenue. A precise definition of such systems is difficult to provide both because of the ambitious scope of the applications that are envisaged and because of the fact that the boundaries of this research area are still fluid. Roughly speaking, such systems consist of two components: (i) physical, biological and engineered systems that are usually large scale and complex, and (ii) a cyber core consisting of communication networks and computational availability that monitors, coordinates and controls the physical system. The crucial component is the tight integration between the two components, so that both the analysis and the design of cyberphysical systems has to be done in a joint framework. Drivers of cyberphysical systems are obvious. As computers become ever-smaller, faster and more efficient, and communication networks become better and ever-cheaper, computing and

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In order to increase the efficiency of the solar photovoltaic (PV) system, accurate electrical modeling of the system under different environmental conditions is necessary. The double diode electrical model of solar PV is known to be more accurate than its single diode model counterpart since it takes into account the effect of recombination. However, because of its nonlinear characteristics, the parameters of the double diode model have to be identified using optimization algorithms. In this paper, the Wind Driven Optimization (WDO) algorithm is proposed as a potential new method for identifying the parameters of a twelveparameter double diode model (12p-DDM) of the solar PV. The accuracy and flexibility of the proposed method are verified using three different sets of data: (i) experimental data at the controlled environmental condition, (ii) data sheet values of different solar PV modules and (iii) real-time experimental data at the uncontrolled environmental condition. Additionally, the performance of the WDO is compared to other well-known existing optimization techniques. The obtained results show that the WDO algorithm can provide optimized values with reduced Mean Absolute Error in Power (MAEP) and reduced Root Mean Square Error (RMSE) for different types of solar PV modules at different environmental conditions. We show that the WDO can be confidently recommended as a reliable optimization algorithm for parameter estimation of solar PV model.

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