Implementation of a Differential Flatness Based Controller on an Open Channel Using a SCADA System

International audienceWith a population of more than 6 billion people, food production from agriculture must be raised to meet increasing demand. While irrigated agriculture provides 40% of the total food production, it represents 80% of the freshwater consumption worldwide. In summer and drought conditions, efficient management of scarce water resources becomes crucial. The majority of irrigation canals are managed manually, however, with large water losses leading to low water efficiency. This article focuses on the development of algorithms that could contribute to more efficient management of irrigation canals that convey water from a source, generally a dam or reservoir located upstream, to water users. We also describe the implementation of an algorithm for real-time irrigation operation using a supervision, control, and data acquisition (SCADA) system with an automatic centralized controller. Irrigation canals can be viewed and modeled as delay systems since it takes time for the water released at the upstream end to reach the user located downstream. We thus present an open-loop controller that can deliver water at a given location at a specified time. The development of this controller requires a method for inverting the equations that describe the dynamics of the canal in order to parameterize the controlled input as a function of the desired output. The Saint-Venant equations [1] are widely used to describe water discharge in a canal. Since these equations are not easy to invert, we consider a simplified model, called the Hayami model. We then use differential flatness to invert the dynamics of the system and to design an open-loop controller

LEARNING AND ESTIMATION APPLICATIONS OF AN ONLINE HOMOTOPY ALGORITHM FOR A GENERALIZATION OF THE LASSO

Abstract. The LASSO is a widely used shrinkage and selection method for linear regression. We propose a generalization of the LASSO in which the l 1 penalty is applied on a linear transformation of the regression parameters, allowing to input prior information on the structure of the problem and to improve interpretability of the results. We also study time varying system with an l 1 -penalty on the variations of the state, leading to estimates that exhibit few "jumps". We propose a homotopy algorithm that updates the solution as additional measurements are available. The algorithm takes advantage of the sparsity of the solution for computational efficiency and is promising for mining large datasets. The algorithm is implemented on three experimental data sets representing applications to traffic estimation from sparsely sampled probe vehicles, flow estimation in tidal channels and text analysis of on-line news. Least-squares regression with l 1 -norm regularization is known as the LASSO algorith

Topics in Large-Scale Sparse Estimation and Control

In this thesis, we study two topics related to large-scale sparseestimation and control. In the first topic, we describe a method toeliminate features (variables) in $\ell_{1}$-regularized convex optimizationproblems. The elimination of features leads to a potentially substantialreduction in computational effort needed to solve such problems, especiallyfor large values of the penalty parameter. Our method is not heuristic:it only eliminates features that are guaranteed to be absent aftersolving the optimization problem. The feature elimination step iseasy to parallelize and can test each feature for elimination independently.Moreover, the computational effort of our method is negligible comparedto that of solving the convex problem.We study the case of $\ell_{1}$-regularized least-squares problem(a.k.a. LASSO) extensively and derive a closed-form sufficient conditionfor eliminating features. The sufficient condition can be evaluatedby few vector-matrix multiplications. For comparison purposes, wepresent a LASSO solver that integrates SAFE with the Coordinate Descentmethod. We call our method CD-SAFE, and we report the number of computationsneeded for solving a LASSO problem using CD-SAFE and using the plainCoordinate Descent method. We observe at least a $100$ fold reductionin computational complexity for dense and sparse data-sets consistingof millions of variables and millions of observations. Some of thesedata-sets can cause memory problems when loaded, or need specializedsolvers. However, with SAFE, we can extend LASSO solvers capabilitiesto treat large-scale problems, previously out of their reach. Thisis possible, because SAFE eliminates variables and thus portions ofour data at the outset, before loading it into our memory.We also show how our method can be extended to general $\ell_{1}$-regularizedconvex problems. We present preliminary results for the Sparse SupportVector Machine and Logistic Regression problems.In the second topic of the thesis, we derive a method for open-loopcontrol of open channel flow, based on the Hayami model, a parabolicpartial differential equation resulting from a simplification of theSaint-Venant equations. The open-loop control is represented as infiniteseries using differential flatness, for which convergence is assessed.Numerical simulations show the effectiveness of the approach by applyingthe open-loop controller to irrigation canals modeled by the fullSaint-Venant equations. We experiment with our controller on the Gignac Canal, located northwestof Montpellier, in southern France. The experiments show that it ispossible to achieve a desired water flow at the downstream of a canalusing the Hayami model as an approximation of the real-system. However,our observations of the measured water flow at the upstream controlledgate made us realize some actuator limitations. For example, deadbandin the gate opening and unmodeled disturbances such as friction inthe gate-opening mechanism, only allow us to deliver piece-wise constantcontrol inputs. This fact made us investigate a way to compute a controllerthat respects the actuator limitations. We use the CD-SAFE algorithm,to compute such open-loop control for the upstream water flow. Wecompare the computational effort needed to obtain an open-loop controlwith certain dynamics using the CD-SAFE algorithm and the plain CoordinateDescent algoirthm. We show that with CD-SAFE we are able to obainan open-loop control signal with cheaper computations

DNA methylation signatures of Prostate Cancer in peripheral T-cells

Abstract Background Prostate Cancer (PCa) is the second most common cancer in men where advancements have been made for early detection using imaging techniques, however these are limited by lesion size. Immune surveillance has emerged as an effective approach for early detection and to monitor disease progression. In recent studies, we have shown that host peripheral blood immune cells undergo changes in DNA methylation in liver and breast cancer. Methods In the current study, we examined the DNA methylation status of peripheral blood T cells of men with positive biopsy for PCa versus men with negative biopsy having benign prostate tissue, defined as controls. T cells DNA was isolated and subjected to Illumina Infinium methylation EPIC array and validated using Illumina amplicon sequencing and pyrosequencing platforms. Results Differential methylation of 449 CG sites between control and PCa T cell DNA showed a correlation with Gleason score (p < 0.05). Two hundred twenty-three differentially methylated CGs between control and PCa (∆ß +/− 10%, p < 0.05), were enriched in pathways involved in immune surveillance system. Three CGs which were found differentially methylated following DMP (Differentially methylated probes) analysis of ChAMP remained significant after BH (Benjamini-Hochberg) correction, of which, 2 CGs were validated. Predictive ability of combination of these 3 CGs (polygenic methylation score, PMS) to detect PCa had high sensitivity, specificity and overall accuracy. PMS also showed strong positive correlation with Gleason score and tumor volume of PCa patients. Conclusions Results from the current study provide for the first-time a potential role of DNA methylation changes in peripheral T cells in PCa. This non-invasive methodology may allow for early intervention and stratification of patients into different prognostic groups to reduce PCa associated morbidity from repeat invasive prostate biopsies and design therapeutic strategy to reduce PCa associated mortality