Modeling,analysis and compensation of disturbances during task execution of a wheeled inverted pendulum type assistant robot using a unified controller

This paper presents the modeling and analysis of disturbances caused by model changes, unknown dynamics, and external forces during task execution of a wheeled Inverted PENdulum Type Assistant Robot. Compensating these disturbances and their effects results in the ability of the system to perform different tasks without changes in the control structure. This results in a unified controller for multiple tasks making the control system simple without sacrificing robustness. Estimation of these disturbances is performed using the extended state observer (ESO), which is a practical observer since it requires very little system information. To ensure proper application of the ESO, the analysis of the equilibrium states under disturbances was performed. The disturbance modeling and the effectiveness of the control are verified by results from disturbance and task tests. Finally, the bounded input bounded output behavior of the system with consideration to the disturbances is presented.     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.     

Sequential interval motif search: unrestricted database surveys of global MS/MS data sets for detection of putative post-translational modifications

Tandem mass spectrometry is the prevailing approach for large-scale peptide sequencing in high-throughput proteomic profiling studies. Effective database search engines have been developed to identify peptide sequences from MS/MS fragmentation spectra. Since proteins are polymorphic and subject to post-translational modifications (PTM), however, computational methods for detecting unanticipated variants are also needed to achieve true proteome-wide coverage. Different from existing "unrestrictive" search tools, we present a novel algorithm, termed SIMS (for Sequential Motif Interval Search), that interprets pairs of product ion peaks, representing potential amino acid residues or "intervals", as a means of mapping PTMs or substitutions in a blind database search mode. An effective heuristic software program was likewise developed to evaluate, rank, and filter optimal combinations of relevant intervals to identify candidate sequences, and any associated PTM or polymorphism, from large collections of MS/MS spectra. The prediction performance of SIMS was benchmarked extensively against annotated reference spectral data sets and compared favorably with, and was complementary to, current state-of-the-art methods. An exhaustive discovery screen using SIMS also revealed thousands of previously overlooked putative PTMs in a compendium of yeast protein complexes and in a proteome-wide map of adult mouse cardiomyocytes. We demonstrate that SIMS, freely accessible for academic research use, addresses gaps in current proteomic data interpretation pipelines, improving overall detection coverage, and facilitating comprehensive investigations of the fundamental multiplicity of the expressed proteome.     

Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

The closed loop control of blood glucose levels might help to reduce many short- and long-term complications of type 1 diabetes. Continuous glucose monitoring and insulin pump systems have facilitated the development of the artificial pancreas. In this paper, artificial neural networks are used for both the identification of patient dynamics and the glycaemic regulation. A subcutaneous glucose measuring system together with a Lispro insulin subcutaneous pump were used to gather clinical data for each patient undergoing treatment, and a corresponding in silico and ad hoc neural network model was derived for each patient to represent their particular glucose-insulin relationship. Based on this nonlinear neural network model, an ad hoc neural network controller was designed to close the feedback loop for glycaemic regulation of the in silico patient. Both the neural network model and the controller were tested for each patient under simulation, and the results obtained show a good performance during food intake and variable exercise conditions.     

A harmonic balance approach to robust neural control of MIMO nonlinear processes applied to a distillation column

Robust stability and performance are the two most basic features of feedback control process. The harmonic balance analysis based on the describing function technique enables to analyze the stability of limit cycles arising from a closed loop control process operating over nonlinear plants. In this work a robust stability analysis based on the harmonic balance is presented and applied to a neural network controller in series with a dynamic multivariable nonlinear plant under generic Lur’e configuration. The neural controller is replaced by its sinusoidal input describing function while a linearized model is derived to represent the nonlinear plant dynamics. The uncertainty induced by the high harmonics effect for the neural controller, together with the neglected nonlinear dynamics due to plant linearization are incorporated in the robustness analysis as structured norm bounded uncertainties. Stability and robustness conditions for the neural closed loop control system are discussed using the harmonic balance equation together with the structured singular values of the uncertainty. The application to a multivariable binary distillation column under feedback neurocontrol illustrates the usefulness of the robustness approach here developed to predict the absence of limit cycles, which of course is subject to the usual restrictions of the describing function method.     

Intracranial ependymomas in children: a critical review of prognostic factors and a plea for cooperation

BACKGROUND: Current controversies in pediatric intracranial ependymoma include histologic categorization and management. Most of our knowledge of this disease comes from single-institution reports. METHODS: A literature search was done, covering the period 1976-1996. The aim of this review is to analyze the prognostic factors reported in the literature over the last 20 years. RESULTS: Forty-five series were reviewed, including more than 1,400 children. The largest series reported on 92 patients, and the accrual rate ranged from 0.32-12 patients per year. None of the prognostic factors reported achieved a consensus throughout the different series. Histology remains a major issue, and the range in the incidence of anaplastic ependymo mas (7-89%) highlights the difficulty in agreeing on a histological grading system. The role of surgery on the outcome seems to be determinant. Recent series based on homogeneous imaging-documented extents of resection strongly support the benefit of postoperative radiotherapy. The lack of a proven, effective chemotherapy regimen precludes its use except in prospective pilot studies. CONCLUSIONS: Limited information is available from single-institution reports in ependymoma. Only large national or international studies can provide enough information to allow a multivariate analysis of the prognostic factors, and thus lead to new therapeutic proposals.     

Indirect adaptive structure for multivariable neural identification and control of a pilot distillation plant

This paper describes the design and implementation of an indirect adaptive controller that uses neural networks both for identification and control of an experimental pilot distillation column containing a mixture of ethanol and water. The MATLAB platform is applied both for the neural identification and control of the distillation plant using the Levenberg–Marquardt approach, enabling also optimal input/output net configuration. The neural controller performance has been analyzed and illustrated via experimental tests on the pilot distillation column monitored under the LabVIEW platform. Both platforms have been linked together by constituting an integrated process control interface. The obtained experimental results demonstrate the effectiveness of the neural indirect adaptive control scheme as compared to proportional–integrative–derivative, when real-time multivariable control is demanded, even in presence of disturbances.     

Modeling and evaluation of the indoor power line transmission medium

The aim of this article is to outline the possibilities of indoor power lines when used to support local area networks in homes or small offices. For this purpose, the channel characteristics are described and a channel model is presented. This model is related to the physical nature of common indoor power lines, so its parameters can be defined in a straightforward way. Based on it, the performance of communication systems that use discrete multitone modulation, which appears to be the most suitable technique for these channels, is evaluated. Finally, a discussion about medium access control strategies is included.