Model reduction of controlled Fokker--Planck and Liouville-von Neumann equations

Model reduction methods for bilinear control systems are compared by means of practical examples of Liouville-von Neumann and Fokker--Planck type. Methods based on balancing generalized system Gramians and on minimizing an H2-type cost functional are considered. The focus is on the numerical implementation and a thorough comparison of the methods. Structure and stability preservation are investigated, and the competitiveness of the approaches is shown for practically relevant, large-scale examples

Closed-loop control of an open cavity flow using reduced-order models

International audienceThe control of separated fluid flow by reduced-order models is studied using the two-dimensional incompressible flow over an open square cavity at Reynolds numbers where instabilities are present. Actuation and measurement locations are taken on the upstream and downstream edge of the cavity. A bi-orthogonal projection is introduced to arrive at reduced-order models for the compensated problem. Global modes, proper orthogonal decomposition (POD) modes and balanced modes are used as expansion bases for the model reduction. The open-loop behaviour of the full and the reduced systems is analysed by comparing the respective transfer functions. This analysis shows that global modes are inadequate to sufficiently represent the inputoutput behaviour whereas POD and balanced modes are capable of properly approximating the exact transfer function. Balanced modes are far more efficient in this process, but POD modes show superior robustness. The performance of the closed-loop system corroborates this finding: while reduced-order models based on POD are able to render the compensated system stable, balanced modes accomplish the same with far fewer degrees of freedom. © 2009 Cambridge University Press

On Modeling and Nonlinear Model Reduction in Automotive Systems

The current control design development process in automotive industry and elsewhere involves many expensive experiments and hand-tuning of control parameters. Model based control design is a promising approach to reduce costs and development time. In this process low complexity models are essential and model reduction methods are very useful tools. This thesis combines the areas of modeling and model reduction with applications in automotive systems. A model reduction case study is performed on an engine air path. The heuristic method commonly used when modeling engine dynamics is compared with a more systematic approach based on the balanced truncation method.The main contribution of this thesis is a method for model reduction of nonlinear systems. The procedure is focused on reducing the number of states using information obtained by linearization around trajectories. The methodology is closely tied to existing theory on error bounds and good results are shown in form of examples such as a controller used in real-world cars. Also, a model of the exhaust gas oxygen sensor, used for air-fuel ratio control in automotive spark-ignition engines, is developed and successfully validated

Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location

This research presents the implementation of optimization algorithms to build auxiliary signals that can be injected as inputs into a pipeline in order to estimate —by using state observers—physical parameters such as the friction or the velocity of sound in the fluid. For the state estimator design, the parameters to be estimated are incorporated into the state vector of a Liénard-type model of a pipeline such that the observer is constructed from the augmented model. A prescribed observability degree of the augmented model is guaranteed by optimization algorithms by building an optimal input for the identification. The minimization of the input energy is used to define the optimality of the input, whereas the observability Gramian is used to verify the observability. Besides optimization algorithms, a novel method, based on a Liénard-type model, to diagnose single and sequential leaks in pipelines is proposed. In this case, the Liénard-type model that describes the fluid behavior in a pipeline is given only in terms of the flow rate. This method was conceived to be applied in pipelines solely instrumented with flowmeters or in conjunction with pressure sensors that are temporarily out of service. The design approach starts with the discretization of the Liénard-type model spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated prior to the leak). The residual closest to zero will indicate the section where a leak is occurring. The main contribution of our method is that it only requires flow measurements at the pipeline ends, which leads to cost reductions. Some simulation-based tes