Linear System Analysis and Optimal Control of Natural Gas Dynamics in Pipeline Networks

We examine nonlinear and adaptive linear control systems that model compressor-actuated dynamics of natural gas flow in pipeline networks. A model-predictive controller (MPC) is developed for feedback control of compressor actions in which the internal optimization over the local time horizon is constrained by the dynamics of either the nonlinear system or the adaptive linear system. Stability of the local linear system is established and a rigorous bound on the error between the solutions of the nonlinear and linear systems is derived and used to devise situations when the linear MPC may be used instead of the nonlinear MPC without a significant difference between their respective predictions. We use several test networks to compare the performances of various controllers that involve nonlinear and adaptive linear models as well as moving-horizon and single-interval optimization. Our results demonstrate that the proposed moving-horizon MPC is well-equipped to adapt in local time to changes in system parameters and has the ability to reduce total computational costs by orders of magnitude relative to conventional transient optimization methods.