Linear Algebra Based trajectory control

[ES] En este tutorial se resumen las principales características de una nueva metodología de diseño de sistemas de control para el seguimiento de trayectorias en procesos no lineales. Esta metodología, denominada LAB (Linear Algebra Based), fue presentada por los autores hace más de diez años y ha tenido una fuerte repercusión por su sencillez y facilidad de aplicación, si bien no es aplicable para algunos problemas de seguimiento en sistemas no lineales. Se exponen las etapas en el diseño de un controlador LAB, tanto en tiempo continuo como en discreto. La aplicación al control de la trayectoria de un robot móvil, en tiempo continuo, sirve para ilustrar el desarrollo e implementación del control. Se analizan algunas propiedades del sistema controlado y se resaltan las condiciones de aplicación. Numerosas referencias facilitan el desarrollo de algunas características y su aplicación en diversos campos de la robótica y del control de procesos en general.[EN] In this tutorial, the main features of a new control design methodology for tracking control in nonlinear processes is summarized. The so called LAB (Linear Algebra Based) methodology was introduced by the authors more than ten years ago and it has been accepted and used by many researchers mainly due to its simplicity and easy application. Nevertheless, it is not applicable to all the tracking problems dealing with nonlinear systems. The LAB controller design procedure, both in continuous time and discretetime, is outlined. The design of the trajectory control of a mobile robot illustrates the procedure as well as its implementation. Some properties of the controlled process are discussed and the problem requirements for a successful application are pointed out. Several references allow a deeper analysis of the controlled plant features as well as its application in a variety of processes, either in robotics or in process control.Scaglia, GJE.; Serrano, ME.; Albertos Pérez, P. (2020). Control de trayectorias basado en álgebra lineal. Revista Iberoamericana de Automática e Informática industrial. 17(4):344-353. https://doi.org/10.4995/riai.2020.13584OJS344353174Apostol, T., 1967. CALCULUS, One -Variable Calculus, with an introduction to Linear Algebra. Blaisdell Publishing Company.Battilotti, S., Califano, C., 2004. A constructive condition for dynamic feedback linearization. Systems & control letters 52(5), 329-338. https://doi.org/10.1016/j.sysconle.2004.02.009Bouhenchir, H., Cabassud, M., Le Lann, M.-V., 2006. Predictive functional control for the temperature control of a chemical batch reactor. Computers & Chemical Engineering 30 (6-7), 1141-1154. https://doi.org/10.1016/j.compchemeng.2006.02.014Brockett, R., 1965. 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Trajectory tracking controller for a nonlinear fed-batch bioprocess. Revista Ingeniería Electrónica, Automática y Comunicaciones ISSN:1815-5928 38 (1), 78.Proaño, P., Capito, L., Rosales, A., Camacho, O., 2015. Sliding mode control:Implementation like pid for trajectory-tracking for mobile robots. In: 2015 Asia-Pacific Conference on Computer Aided System Engineering. IEEE, pp.220-225. https://doi.org/10.1109/APCASE.2015.46Rojas, O. J., Goodwin, G. C., 2001. Preliminary analysis of a nonlinear control scheme related to feedback linearization. In Proceedings of the 40th IEEE Conference on Decision and Control 2, 1743-1748.Rosales, A., Scaglia, G., Mut, V., di Sciascio, F., 2009. Navegación de robots móviles en entornos no estructurados utilizando álgebra lineal. Revista Iberoamericana de Automática e Informática Industrial RIAI, 6(2), 79-88. https://doi.org/10.1016/S1697-7912(09)70096-2Rosales, C., Gandolfo, D., Scaglia, G., Jordan, M., Carelli, R., 2015. Trajectory tracking of a mini four-rotor helicopter in dynamic environments-a linear algebra approach. Robotica 33 (8), 1628-1652. https://doi.org/10.1017/S0263574714000952Scaglia, G., Montoya, L. Q., Mut, V., di Sciascio, F., 2009. Numerical methods based controller design for mobile robots. Robotica 27 (2), 269-279. https://doi.org/10.1017/S0263574708004669Scaglia, G., Quintero, O. L., Mut, V., di Sciascio, F., 2008. Numerical methods based controller design for mobile robots. IFAC Proceedings Volumes 41 (2), 4820 - 4827. https://doi.org/10.3182/20080706-5-KR-1001.00810Scaglia, G., Serrano, E., Rosales, A., Albertos, P., 2015. Linear interpolation based controller design for trajectory tracking under uncertainties: Application to mobile robots. Control Engineering Practice 45, 123-132. https://doi.org/10.1016/j.conengprac.2015.09.010Scaglia, G., Serrano, E., Rosales, A., Albertos, P., 2019. Tracking control design in nonlinear multivariable systems: Robotic applications. Mathematical Problems in Engineering 2019. https://doi.org/10.1155/2019/8643515Scaglia, G., Serrano, M., Albertos, P., 2020. Linear Algebra Based Controllers: Design and Applications. Springer International Publishing. URL: https://books.google.es/books?id=ELzoDwAAQBAJ , https://doi.org/10.1007/978-3-030-42818-1Serrano, M. E., Godoy, S. A., Quintero, L., Scaglia, G. J., 2017. Interpolation based controller for trajectory tracking in mobile robots. Journal of Intelligent & Robotic Systems 86 (3-4), 569-581. https://doi.org/10.1007/s10846-016-0422-4Serrano, M. E., Scaglia, G. J., Godoy, S. A., Mut, V., Ortiz, O. A., 2013. Trajectory tracking of underactuated surface vessels: A linear algebra approach. IEEE Transactions on Control Systems Technology 22 (3), 1103-1111. https://doi.org/10.1109/TCST.2013.2271505Silverman, L., 1968. Properties and application of inverse systems. 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Exploring the socio-ecological factors behind the (in)active lifestyles of Spanish post-bariatric surgery patients

Purpose: Physical activity (PA) is considered essential for the treatment of morbid obesity and the optimization of bariatric surgery outcomes. The objective of this article was to identify the facilitators and barriers that bariatric patients perceived to do PA one year after finishing a PA programme for the promotion of a long-term active lifestyle. This objective was addressed from a socio-ecological and qualitative perspective. Methods: Nine patients (eight women and one man), aged between 31 and 59 years, participated in semi-structured interviews directly following the PA programme and one year after it. A content analysis was carried out to analyze the qualitative data. Results: Weight loss, improvement of physical fitness, perceived competence, and enjoyment were the main facilitators of PA. Complexes related to skin folds, osteoarthritis, perceived unfavourable weather conditions, lack of social support and economic resources, long workdays, lack of specific PA programmes, and other passive leisure preferences were the main barriers to participate in PA. Conclusions: Results highlight the important interplay between personal, social environmental, and physical environmental factors to explain (in)active behaviours of bariatric patients. The findings of this article could be useful for future research and interventions aimed at promoting PA in bariatric patients

Prevalence of BRCA1 and BRCA2 Jewish mutations in Spanish breast cancer patients

We screened the 185delAG and 5382insC (BRCA1) and the 6174delT (BRCA2) mutation in 298 Spanish women with breast cancer. Two women (one with Sephardic ancestors) presented the 185delAG mutation and the same haplotype reported in Ashkenazim with this mutation. This suggests a common origin of the 185delAG in both Sephardic and Ashkenazi populations. © 1999 Cancer Research Campaig

A non-uniform predictor-observer for a networked control system

The final publication is available at Springer via http://dx.doi.org/10.1007/s12555-011-0621-5This paper presents a Non-Uniform Predictor-Observer (NUPO) based control approach in order to deal with two of the main problems related to Networked Control Systems (NCS) or Sensor Networks (SN): time-varying delays and packet loss. In addition, if these delays are longer than the sampling period, the packet disordering phenomenon can appear. Due to these issues, a (scarce) nonuniform, delayed measurement signal could be received by the controller. But including the NUPO proposal in the control system, the delay will be compensated by the prediction stage, and the nonavailable data will be reconstructed by the observer stage. So, a delay-free, uniformly sampled controller design can be adopted. To ensure stability, the predictor must satisfy a feasibility problem based on a time-varying delay-dependent condition expressed in terms of Linear Matrix Inequalities (LMI). Some aspects like the relation between network delay and robustness/performance trade-off are empirically studied. A simulation example shows the benefits (robustness and control performance improvement) of the NUPO approach by comparison to another similar proposal. © ICROS, KIEE and Springer 2011.This work was supported by the Spanish Ministerio de Ciencia y Tecnologia Projects DPI2008-06737-C02-01 and DPI2009-14744-C03-03, by Generalitat Valenciana Project GV/2010/018, by Universidad Politecnica de Valencia Project PAID06-08.Cuenca Lacruz, ÁM.; García Gil, PJ.; Albertos Pérez, P.; Salt Llobregat, JJ. (2011). A non-uniform predictor-observer for a networked control system. International Journal of Control, Automation and Systems. 9(6):1194-1202. doi:10.1007/s12555-011-0621-5S1194120296K. Ogata, Discrete-time Control Systems, Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1987.Y. Tipsuwan and M. Chow, “Control methodologies in networked control systems,” Control Eng. 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Piza, “A delay-dependent dual-rate PID controller over an Ethernet network,” IEEE Trans. Ind. Informat., vol. 7, no. 1, pp. 18–29, Feb. 2011.Y. Tian and D. Levy, “Compensation for control packet dropout in networked control systems,” Inform. Sci., vol. 178, no. 5, pp. 1263–1278, 2008.Y. Zhao, G. Liu, and D. Rees, “Modeling and stabilization of continuous-time packet-based networked control systems.” IEEE Trans. Syst., Man, Cybern. B, vol. 39, no. 6, pp. 1646–1652, Dec. 2009.X. Zhao, S. Fei, and C. Sun, “Impulsive controller design for singular networked control systems with packet dropouts,” Int. J. Control, Autom., and Syst., vol. 7, no. 6, pp. 1020–1025, 2009.H. Gao and T. Chen, “H ∞ estimation for uncertain systems with limited communication capacity,” IEEE Trans. Autom. Control, vol. 52, no. 11, pp. 2070–2084, 2007.S. Oh, L. Schenato, P. Chen, and S. Sastry, “Tracking and coordination of multiple agents using sensor networks: System design, algorithms and experiments,” Proc. of the IEEE, vol. 95, no. 1, pp. 234–254, 2007.M. Moayedi, Y. Foo, and Y. Soh, “Optimal and suboptimal minimum-variance filtering in networked systems with mixed uncertainties of random sensor delays, packet dropouts and missing measurements,” Int. J. Control, Autom., and Syst., vol. 8, no. 6, pp. 1179–1188, 2010.W. Zhang, M. Branicky, and S. Phillips, “Stability of networked control systems,” IEEE Control Syst. Mag., vol. 21, no. 1, pp. 84–99, 2001.J. Hespanha, P. Naghshtabrizi, and Y. Xu, “A survey of recent results in networked control systems,” Proc. of the IEEE, vol. 95, no. 1, pp. 138–162, 2007.J. Baillieul and P. Antsaklis, “Control and communication challenges in networked real-time systems,” Proc. of the IEEE, vol. 95, no. 1, pp. 9–28, 2007.P. Garcia, P. Castillo, R. Lozano, and P. Albertos, “Robustness with respect to delay uncertainties of a predictor-observer based discrete-time controller,” Proc. of the 45th IEEE Conf. on Decision and Control, pp. 199–204, 2006.C. Lien, “Robust observer-based control of systems with state perturbations via LMI approach,” IEEE Trans. Autom. Control, vol. 49, no. 8, pp. 1365–1370, 2004.A. Sala, “Computer control under time-varying sampling period: an LMI gridding approach,” Automatica, vol. 41, no. 12, pp. 2077–2082, Dec. 2005.J. Li, Q. Zhang, Y. Wang, and M. Cai, “H ∞ control of networked control systems with packet disordering,” IET Control Theory Appl., vol. 3, no. 11, pp. 1463–1475, March 2009.Y. Zhao, G. Liu, and D. Rees, “Improved predictive control approach to networked control systems,” IET Control Theory Appl., vol. 2, no. 8, pp. 675–681, Aug. 2008.K. Astrom, “Event based control,” Analysis and Design of Nonlinear Control Systems, pp. 127–147, 2007.A. Cuenca, P. García, K. Arzén, and P. 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Symp. on Mathematical Theory of Networks and Systems, pp. 2163–2170, 2006.D. Davison and E. Hwang, “Automating radiotherapy cancer treatment: use of multirate observer-based control,” Proc. of American Control Conf., vol. 2, pp. 1194–1199, 2003

Testing Gait with Ankle-Foot Orthoses in Children with Cerebral Palsy by Using Functional Mixed-Effects Analysis of Variance

Dr Morrissey is part funded by the NIHR/HEE Senior Clinical Lecturer scheme. Tis report presents independent research part-funded by the National Institute for Health Research (NIHR) CAT SCL-2013-04-00

Codominant scoring of AFLP in association panels

A study on the codominant scoring of AFLP markers in association panels without prior knowledge on genotype probabilities is described. Bands are scored codominantly by fitting normal mixture models to band intensities, illustrating and optimizing existing methodology, which employs the EM-algorithm. We study features that improve the performance of the algorithm, and the unmixing in general, like parameter initialization, restrictions on parameters, data transformation, and outlier removal. Parameter restrictions include equal component variances, equal or nearly equal distances between component means, and mixing probabilities according to Hardy–Weinberg Equilibrium. Histogram visualization of band intensities with superimposed normal densities, and optional classification scores and other grouping information, assists further in the codominant scoring. We find empirical evidence favoring the square root transformation of the band intensity, as was found in segregating populations. Our approach provides posterior genotype probabilities for marker loci. These probabilities can form the basis for association mapping and are more useful than the standard scoring categories A, H, B, C, D. They can also be used to calculate predictors for additive and dominance effects. Diagnostics for data quality of AFLP markers are described: preference for three-component mixture model, good separation between component means, and lack of singletons for the component with highest mean. Software has been developed in R, containing the models for normal mixtures with facilitating features, and visualizations. The methods are applied to an association panel in tomato, comprising 1,175 polymorphic markers on 94 tomato hybrids, as part of a larger study within the Dutch Centre for BioSystems Genomics

Model confidence sets and forecast combination: an application to age-specific mortality

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification