A physics-based approach to flow control using system identification

Control of amplifier flows poses a great challenge, since the influence of environmental noise sources and measurement contamination is a crucial component in the design of models and the subsequent performance of the controller. A modelbased approach that makes a priori assumptions on the noise characteristics often yields unsatisfactory results when the true noise environment is different from the assumed one. An alternative approach is proposed that consists of a data-based systemidentification technique for modelling the flow; it avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design. This technique is applied to flow over a backward-facing step, a typical example of a noise-amplifier flow. Physical insight into the specifics of the flow is used to interpret and tailor the various terms of the auto-regressive model. The designed compensator shows an impressive performance as well as a remarkable robustness to increased noise levels and to off-design operating conditions. Owing to its reliance on only timesequences of observable data, the proposed technique should be attractive in the design of control strategies directly from experimental data and should result in effective compensators that maintain performance in a realistic disturbance environment

Variance estimation of modal parameters from input/output covariance-driven subspace identification

International audienceFor Operational Modal Analysis (OMA), the vibration response of a structure from ambient and unknown ex-citation is measured and used to estimate the modal parameters. For OMA with eXogenous inputs (OMAX), some of the inputs are known in addition, which are considered as realizations of a stochastic process. When identifying the modal parameters from noisy measurement data, the information on their uncertainty is most relevant. Previously, a method for variance estimation has been developed for the output-only case with covariance-driven subspace identification. In this paper, a recent extension of this method for the in-put/output covariance-driven subspace algorithm is discussed. The resulting variance expressions are easy to evaluate and computationally tractable when using an efficient implementation. Based on Monte Carlo simulations, the quality of identification and the accuracy of variance estimation are evaluated. It is shown how the input information leads to better identification results and lower uncertainties

Towards Efficient Maximum Likelihood Estimation of LPV-SS Models

How to efficiently identify multiple-input multiple-output (MIMO) linear parameter-varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling variable still remains an open question, as identification methods proposed in the literature suffer heavily from the curse of dimensionality and/or depend on over-restrictive approximations of the measured signal behaviors. However, obtaining an SS model of the targeted system is crucial for many LPV control synthesis methods, as these synthesis tools are almost exclusively formulated for the aforementioned representation of the system dynamics. Therefore, in this paper, we tackle the problem by combining state-of-the-art LPV input-output (IO) identification methods with an LPV-IO to LPV-SS realization scheme and a maximum likelihood refinement step. The resulting modular LPV-SS identification approach achieves statical efficiency with a relatively low computational load. The method contains the following three steps: 1) estimation of the Markov coefficient sequence of the underlying system using correlation analysis or Bayesian impulse response estimation, then 2) LPV-SS realization of the estimated coefficients by using a basis reduced Ho-Kalman method, and 3) refinement of the LPV-SS model estimate from a maximum-likelihood point of view by a gradient-based or an expectation-maximization optimization methodology. The effectiveness of the full identification scheme is demonstrated by a Monte Carlo study where our proposed method is compared to existing schemes for identifying a MIMO LPV system

Subspace-based Identification of a Parallel Kinematic Manipulator Dynamics

This thesis deals with the identification of the dynamics of a Parallel Kinematic Manipulator, namely the Gantry-Tau patented by ABB located in the Robotics lav at LTH, Lund. The approach considered for modelling is subspace-based identification of linear models, where measurements from the robot motion are used to estimate the unknown parameters in the models. Rigid body dynamics and flexible body dynamics are taken into account and a description of the system in terms of a network with spring-damper pairs at the edges, representing the clusters, and masses at the nodes representing the end-effector and the carts, is proposed

Parameterization of Stabilizing Linear Coherent Quantum Controllers

This paper is concerned with application of the classical Youla-Ku\v{c}era parameterization to finding a set of linear coherent quantum controllers that stabilize a linear quantum plant. The plant and controller are assumed to represent open quantum harmonic oscillators modelled by linear quantum stochastic differential equations. The interconnections between the plant and the controller are assumed to be established through quantum bosonic fields. In this framework, conditions for the stabilization of a given linear quantum plant via linear coherent quantum feedback are addressed using a stable factorization approach. The class of stabilizing quantum controllers is parameterized in the frequency domain. Also, this approach is used in order to formulate coherent quantum weighted $H_2$ and $H_\infty$ control problems for linear quantum systems in the frequency domain. Finally, a projected gradient descent scheme is proposed to solve the coherent quantum weighted $H_2$ control problem.Comment: 11 pages, 4 figures, a version of this paper is to appear in the Proceedings of the 10th Asian Control Conference, Kota Kinabalu, Malaysia, 31 May - 3 June, 201

OPTIMAL FISCAL POLICY IN A BUSINESS CYCLE MODEL: ALTERNATIVE IDENTIFICATIONS OF THE OPTIMAL EXPOST CAPITAL INCOME TAX RATES

This paper deals with the indeterminacy of optimal fiscal policy treated by Zhu (1992) and Chari, Christiano and Kehoe (1994). These authors identify the optimal fiscal policy restricting the debt return to be uncontingent to the state of nature. In this paper we use other kind of restrictions in order to identify the optimal fiscal policy. Using the solution method proposed by Sims (1998), we can select an equilibrium by enforcing a stable path for the bonds allocation, to identify all the fiscal policy variables contingent to the state of nature. We also use a decomposition of the expectational terms that allow us to obtain the ex-ante capital income tax rate in order to be compared with the ex-post (contingent) tax rate. We can demonstrate that the risk aversion changes the relationship between the expectational errors of the private agents and the sources of fluctuations. The numerical simulation provides some different results: the optimal tax rate on capital incom e is constant, instead of the very volatile tax rate obtained by Chari, Christiano and Kehoe (1994). This property remains unaltered when we use alternative restrictions (exogenous debt path and exogenous expectational errors) to identify the contingent optimal fiscal policy.