Data-driven methodologies have recently emerged as an important paradigm alternative to model-based controller design and several such methodologies are formulated as an H2 performance optimization. This book presents a comprehensive theoretical treatment of the H2 approach to data-driven control design. The fundamental properties implied by the H2 problem formulation are analyzed in detail, so that common features to all solutions are identified. Direct methods (VRFT) and iterative methods (IFT, DFT, CbT) are put under a common theoretical framework. The choice of the reference model, the experimental conditions, the optimization method to be used, and several other designer?s choices are crucial to the quality of the final outcome, and firm guidelines for all these choices are derived from the theoretical analysis presented. The practical application of the concepts in the book is illustrated with a large number of practical designs performed for different classes of processes: thermal, fluid processing and electromechanical.

The design of Assistive Technology products has different technological routes such as massive products or custom products. These products aim at optimize the altered bodily functions. With that it can re-conduct these people to varied social activities. In this sense, the present article focuses on contribute in the construction of new technological routes for the manufacturing of wheelchair custom seats. The methodology includes the application of concepts such as tridimensional digitizing, CNC machining and thermography. Those techniques have the purpose of generating and analyzing seats made from user’s anthropometric data.

The aim of the study was to determine factors associated with changes in self-reported dentifrice consumption in an urban population group over 13 years. This study evaluated two surveys of 671 and 688 households sampled in the urban area of a city from Southern Brazil in 1996 and 2009, respectively. The mother of the family was asked to answer a structured questionnaire about demographics, socioeconomic and behavioral variables. The primary outcome was obtained by questioning "how long does a dentifrice tube last in your house?" The cut-off point of duration was less than 1 month. It was used to determine high consumption of dentifrice (HCD). Associations between HCD and independent variables were evaluated by multivariable Poisson regression. There was a significant decrease of 20% (81.2% to 61.2%) in the prevalence of HCD between 1996 and 2009, resulting in a crude annual decrease of 1.54%. Mother's age, family income, dental assistance, mother's brushing frequency and number of household members that use a toothbrush were significantly associated with HCD independent from the year of survey. The prevalence ratio (PR) of HCD for the year of survey was 0.75, indicating an overall decrease of 25% in the probability of HCD from 1996 to 2009. Probabilities of HCD also decreased over the 13 years among the strata of education, number of household members and reason for choice of dentifrice. It may be concluded that the factors associated with the observed decrease were higher educational levels, larger number of household members and reasons for choosing a dentifrice related to preventive/therapeutic effects.

The Prediction Error Method is related to a non-convex optimization problem. It is usual to apply iterative algorithms to solve this optimization problem. However, iterative algorithms can get stuck at a local minimum of the cost function or converge to the border of the searching space. An analysis of the cost function and sufficient conditions to ensure the convergence of the iterative algorithms to the global minimum are presented in this work. It is observed that this conditions depend on the spectrum of the input signal used in the experiment. This work presents tools to improve the convergence of the algorithms to the global minimum, which are based on the manipulation of the input spectrum.

This work presents a non-linear identification of a bioreactor through the minimization of the prediction error, where the output data are the measurements of the methane gas generated by the process, during 37 days. Since the chosen model is non-linear, an iterative method is used to obtain the model parameters. This method depends on the cost function?s gradient, whose calculus is implemented recursively, since it does not have a closed form. The algorithm used in the minimization of the cost function is a combination of two methods: the gradient method and the Newton-Raphson method. The model obtained is validated with output data from the process and it reproduces the behavior of the bioreactor with good precision.

This work solves an experiment design problem for a linear regression problem using a reduced order model. The quality of the model is assessed using a mean square error measure that depends linearly on the parameters. The designed input signal ensures a predefined quality of the model while minimizing the input energy.

This paper studies a one-shot (non-iterative) data-based method for Model Reference (MR) control design. It shows that the optimal controller can be obtained as the solution of a Prediction Error (PE) identification problem that directly estimates the controller parameters through a reparametrization of the input-output model. The standard tools of PE Identification can thus be used to analyze the statistical properties (bias and variance) of the estimated controller. It also shows that, for MR control design, direct and indirect data-based methods are essentially equivalent.

The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The existence of local minima, and hence the difficulty in solving the optimization, depends mainly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. It is therefore possible to avoid the existence of local minima by properly choosing the spectrum of the input; in this paper we show how to perform this choice. We present sufficient conditions for the convergence of PEM to the global minimum and from these conditions we derive two approaches to avoid the existence of nonglobal minima. We present the application of one of these two approaches to a case study where standard identification toolboxes tend to get trapped in nonglobal minima.

Data-based control design methods most often consist of iterative adjustment of the controller?s parameters towards the parameter values which minimize an Formula performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization algorithm ? no process model is used. Two topics are important regarding this algorithm: the convergence rate and the convergence to the global minimum. This paper discusses these issues and provides a method for choosing the step size to ensure convergence with high convergence rate, as well as a test to verify at each step whether or not the algorithm is converging to the global minimum.