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Mathematical models of energy systems have been mostly represented by either linear or nonlinear ordinary differential equations. This is consistent with lumped-parameter dynamic system modeling, where dynamics of system state variables can be fully described only in the time domain. However, when dynamic processes of energy systems display both temporal and spatial evolutions (as is the case of distributed-parameter systems), the use of partial differential equations is necessary. Distributed-parameter systems, being described by partial differential equations, are mathematically (and computationally) much more difficult for modeling, analysis, simulation, and control. Despite these difficulties in recent years, quite a significant number of papers that use partial differential equations to model and control energy processes and systems have appeared in journal and conference publications and in some books. As a matter of fact, distributed-parameter systems are a modern trend in the areas of control systems engineering and some energy systems. In this overview, we will limit our attention mostly to renewable energy systems, particularly to partial differential equation modeling, simulation, analysis, and control papers published on fuel cells, wind turbines, solar energy, batteries, and wave energy. In addition, we will indicate the state of some papers published on tidal energy systems that can be modelled, analyzed, simulated, and controlled using either lumped or distributed-parameter models. This paper will first of all provide a review of several important research topics and results obtained for several classes of renewable energy systems using partial differential equations. Due to a substantial number of papers published on these topics in the past decade, the time has come for an overview paper that will help researchers in these areas to develop a systematic approach to modeling, analysis, simulation, and control of energy processes and systems whose time–space evolutions are described by partial differential equations. The presented overview was written after the authors surveyed more than five hundred publications available in well-known databases such as IEEE, ASME, Wiley, Google, Scopus, and Web of Science. To the authors’ best knowledge, no such overview on PDEs for energy systems is available in the scientific and engineering literature. Throughout the paper, the authors emphasize novelties, originalities, and new ideas, and identify open problems for future research. To achieve this goal, the authors reviewed more than five hundred journal articles and conference papers.

Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade.

This paper presents a Proton-Exchange Membrane Fuel Cell (PEMFC) transient model in stack current cycling conditions and its partial optimal control. The derived model is used for a specific application of the recently published multistage control technique developed by the authors. The presented control-oriented transient PEMFC model is an extension of the steady-state control-oriented model previously established by the authors. The new model is experimentally validated for transient operating conditions on the Greenlight Innovation G60 testing station where the comparison of the experimental and simulation results is presented. The derived five-state nonlinear control-oriented model is linearized, and three clusters of eigenvalues can be clearly identified. This specific feature of the linearized model is known as the three timescale system. A novel multistage optimal control technique is particularly suitable for this class of systems. It is shown that this control technique enables the designer to construct a local LQR, pole-placement or any other linear controller type at the subsystem level completely independently, which further optimizes the performance of the whole non-decoupled system.

In this study the authors present a novel technique for control of signal power transients caused by signal add/drops in optical communication networks with erbium-doped fibre amplifiers (EDFAs). The approach utilises the electronic automatic gain control that combines both feedback and feedforward control of EDFA. Feedback control is non-linear integral control and feedforward control is a steady-state gain scheduling technique. The feedback non-linear control requires information about the average inversion level that is obtained via a simple linearised first-order observer. The scheduling variable used is the nominal pump power needed to keep the average inversion level at the desired value at steady state and the input signal powers given at particular wavelengths. In the simplified gain scheduling version, the scheduling variable is the total input signal power. Simulation examples indicate that the proposed controller completely eliminates at steady state the photon power transients caused by signal add/drops with the steady state being reached within a few milliseconds.

In this paper we consider a mathematical model of HIV-virus dynamics and propose an efficient control strategy to keep the number of HIV virons under a pre-specified level and to reduce the total amount of medications that patients receive. The model considered is a nonlinear third-order model. The third-order model describes dynamics of three most dominant variables: number of healthy white blood cells (T-cells), number of infected T-cells, and number of virus particles. There are two control variables in this model corresponding to two categories of antiviral drugs: reverse transcriptase inhibitors (RTI) and protease inhibitors (PI). The proposed strategy is based on linearization of the nonlinear model at the equilibrium point (steady state). The corresponding controller has two components: the first one that keeps the system state variables at the desired equilibrium (set-point controller) and the second-one that reduces in an optimal way deviations of the system state variables from their desired equilibrium values. The second controller is based on minimization of the square of the error between the actual and desired (equilibrium) values for the linearized system (linear-quadratic optimal controller). The obtained control strategy recommends to HIV researchers and experimentalists that the constant dosages of drugs have to be administrated at all times (set point controller, open-loop controller) and that the variable dosages of drugs have to be administrated on a daily basis (closed-loop controller, feedback controller).

In this paper we show how to separate the slow and fast dynamics of the disparity convergence of the eye movements dynamic model. The dynamic equations obtained determine the modified slow dynamics that takes into account the impact of the fast dynamics and the modified fast dynamics that takes into account the impact of the slow dynamics. The slow fast decoupling is achieved by finding analytical solutions of the transformation equations used. The transformed slow and fast subsystems have very simple forms. Having separated the slow and fast dynamics completely, neural control problems for the slow and fast eye movements dynamics can be independently studied and better understood.