Guaranteed cost control analysis and iterative design for constrained Takagi–Sugeno systems

This paper presents a novel control design technique in order to obtain a guaranteed cost fuzzy controller subject to constraints on the input channel. This guaranteed cost control law is obtained via multi-parametric quadratic programming. The result is a piecewise fuzzy control law where the state partition is defined by fuzzy inequalities. The parameters of the Lyapunov function can be obtained previously using Linear Matrix Inequalities optimization.     

A technique for defining agent-oriented engineering processes with tool support

The formalization of engineering processes is necessary for guaranteeing the quality and cost of the products involved. Agent-oriented engineering has already proposed development processes that still need to be further formalized to be applicable by non-researchers. This paper proposes a technique to instantiate processes for specific agent-oriented methodologies. This technique is based on three orthogonal views that are respectively related with lifecycles, disciplines and guidances. In addition, processes are modeled with a tool, which is automatically generated from a process metamodel inspired by the software & systems process engineering metamodel. Accordingly, engineers can choose the methodology–process pair best-suited for the characteristics of their project. The paper illustrates the approach based on the unified development process and the scrum process for the INGENIAS methodology and compares the results with other existing alternatives.     

Batch process monitoring in the original measurement's space

Quality control and safety related issues have become more and more important in industrial production of high added value products and chemical specialities during last years. In this regard, many successful applications of multivariate statistical process control (MSPC) for monitoring and diagnosis of batch processes have been presented. It is a common industrial practice to monitor the batch progress by exploiting the information contained in a historical database of successful batches using projection techniques such as principal components analysis (PCA), partial least squares (PLS) and independent component analysis (ICA). In this work, a new MSPC strategy for batch process monitoring is presented. Its distinctive feature is that it works in the space of the original variables. The technique uses only the T²-statistic for detection and identification purposes. The identification of the set of observations that signal the fault is accomplished by decomposing the T²-statistic as a unique sum of each variable contribution. Performance comparisons among the proposed strategy and the most popular PCA-based approaches are carried out by simulation of polymerization and penicillin cultivation batch processes. Results show that the new approach can be successfully applied to monitor this kind of processes since it works very well during both fault detection and identification stages.     

On the design of multi‐rate tracking controllers: Application to rotorcraft guidance and control

This paper presents a new methodology for the design and implementation of gain‐scheduled controllers for multi‐rate systems. The proposed methodology provides a natural way to address the integrated guidance and control problem for autonomous vehicles when the outputs are sampled at different instants of time. A controller structure is first proposed for the regulation of non‐square multi‐rate systems with more measured outputs than inputs. Based on this structure, an implementation for a gain‐scheduled controller is obtained that satisfies an important property known as the linearization property. The implementation resembles the velocity implementation for single‐rate systems. The method is then applied to the problem of steering an autonomous rotorcraft along a predefined trajectory defined in terms of space and time coordinates. By considering a convenient error vector to describe the vehicle's dynamics, the trajectory tracking problem is reduced to that of regulating the error variables to zero. Because of the periodic multi‐rate nature of the onboard sensor suite, the controller synthesis is dealt with under the scope of linear periodic systems theory. Simulation results obtained with a full non‐linear rotorcraft dynamic model are presented and discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Parallel Approach for Ensemble Learning with Locally Coupled Neural Networks

Ensemble learning has gained considerable attention in different tasks including regression, classification and clustering. Adaboost and Bagging are two popular approaches used to train these models. The former provides accurate estimations in regression settings but is computationally expensive because of its inherently sequential structure, while the latter is less accurate but highly efficient. One of the drawbacks of the ensemble algorithms is the high computational cost of the training stage. To address this issue, we propose a parallel implementation of the Resampling Local Negative Correlation (RLNC) algorithm for training a neural network ensemble in order to acquire a competitive accuracy like that of Adaboost and an efficiency comparable to that of Bagging. We test our approach on both synthetic and real datasets from the UCI and Statlib repositories for the regression task. In particular, our fine-grained parallel approach allows us to achieve a satisfactory balance between accuracy and parallel efficiency.     

The fractional Fourier transform and quadratic field magnetic resonance imaging

The fractional Fourier transform (FrFT) is revisited in the framework of strongly continuous periodic semigroups to restate known results and to explore new properties of the FrFT. We then show how the FrFT can be used to reconstruct Magnetic Resonance (MR) images acquired under the presence of quadratic field inhomogeneity. Particularly, we prove that the order of the FrFT is a measure of the distortion in the reconstructed signal. Moreover, we give a dynamic interpretation to the order as time evolution of a function. We also introduce the notion of ρ-α space as an extension of the Fourier or k-space in MR, and we use it to study the distortions introduced in two common MR acquisition strategies. We formulate the reconstruction problem in the context of the FrFT and show how the semigroup theory allows us to find new reconstruction formulas for discrete sampled signals. Finally, the results are supplemented with numerical examples that show how it performs in a standard 1D MR signal reconstruction.     

Numerical identification of a nonlinear diffusion coefficient by discrete mollification

The discrete mollification method is a convolution-based filtering procedure suitable for the regularization of ill-posed problems. Combined with explicit space-marching finite difference schemes, it provides stability and convergence for a variety of coefficient identification problems in linear parabolic equations. In this paper, we extend such a technique to identify some nonlinear diffusion coefficients depending on an unknown space dependent function in one dimensional parabolic models. For the coefficient recovery process, we present detailed error estimates and to illustrate the performance of the algorithms, several numerical examples are included.     

Autonomous motion planning of a hand-arm robotic system based on captured human-like hand postures

The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions and trying to mimic real human hand postures. The approach uses the concept of “principal motion directions” to reduce the dimension of the search space in order to obtain results with a compromise between motion optimality and planning complexity (time). Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a probabilistic roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) assembled on an industrial robot (6 independent degrees of freedom), and experimental examples are included to illustrate its validity.     

EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network

This paper presents a Web 2.0 Learning Environment, for a systematic creation of adaptive and intelligent tutoring systems. Authoring contents is made by a community of users including teachers and students. The tutoring systems adapt the contents according to the best learning style using self-organizing maps (SOMs). The SOM was trained for classifying Felder–Silverman learning styles. The most important advantage of these unsupervised neural networks is that they do not require an external teacher for presenting a training set. The approach was implemented under an authoring tool that allows the production of personalized learning material to be used under collaborative and mobile learning environments. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the authoring tool.