Fluid networks are characterized by complex interconnected flows, involving high order nonlinear dynamics and transport phenomena. Classical lumped models typically capture the interconnections and nonlinear effects but ignore the transport phenomena, which may strongly affect the transient response. To control such flows with regulators of reduced complexity, we improve a classical lumped model (obtained by combining Kirchhoff's laws and graph theory) by introducing the effect of advection as a time delay. The model is based on the isothermal Euler equations to describe the dynamics of the fluid through the pipe. The resulting hyperbolic system of partial differential equations (PDEs) is diagonalized using Riemann invariants to find a solution in terms of delayed equations, obtained analytically using the method of the characteristics. Conservation principles are applied at the nodes of the network to describe the dynamics as a set of (possibly non linear) delay differential equations. Both linearized and nonlinear Euler equations are considered.
This work considers the stabilization and control of a class of unstable first-order linear systems subject to a relatively large input−output time delay. As a first step, a particular observer scheme is proposed in order to predict a specific internal signal in the process. Conditions to ensure the adequate prediction convergence of the signals are formally stated. In a second step, the internal predicted signal is used to implement classical P, PI, and PID controllers providing stability conditions for the resulting closed-loop system. The proposed control strategy allows one to address time delays as large as four times the unstable time constant of the open-loop system, in contrast with the reported related literature where the maximal bound has been stated until now as two times the unstable time constant of the open-loop system. The proposed observer-based control structure considers also the tracking of step reference signals and the rejection of input step disturbances.
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