Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

In this paper, an adaptive chattering free neural network‐based sliding mode control (ACFN‐SMC) method is proposed for tracking trajectories of redundant parallel manipulators. ACFN‐SMC combines adaptive chattering free radial basis function neural networks (RBFN), sliding mode control with online updating the robust term parameters, and a nonlinear compensation item for reducing tracking errors. The stability of the closed‐loop system with modeling uncertainties, frictional uncertainties, and external disturbances is ensured by using the Lyapunov method. The proposed controller has a simple structure and little computation time while securing dynamic performance with expected quality in tracking trajectories of redundant parallel manipulators. In addition, the ACFN‐SMC strategy does not need to know the upper bound of any uncertainties. From the simulation results, it is evident that the proposed control strategy not only has significantly higher robustness capability for uncertainties but also can achieve better chattering elimination when compared with those using existing intelligent control schemes.     

Large-Scale CFD Data Handling in a VR-Based Otorhinolaryngological CAS-System using a Linux-Cluster

Many of the operations to eliminate complaints concerning respiration impairments fail. In order to improve the success rate, it is important to recognize the responsiveness of the flow field within the nasal cavities. Therefore, we are developing a computer assisted surgery (CAS) system that combines computational fluid dynamics (CFD) and virtual reality (VR) technology. However, the primary prerequisite for VR-based applications is real-time interaction. A single graphics workstation is not capable of satisfying this condition and of simultaneously calculating flow features employing the huge CFD data set. In this paper, we will present our approach of a distributed system that relieves the load on the graphics workstation and makes use of an off-the-shelf parallel Linux cluster calculating streamlines. Moreover, we introduce first results and discuss remaining difficulties.     

Trailer truck trajectory optimization: the transportation of components for the Airbus A380

European aircraft manufacturer, Airbus, has started a program to develop the largest aircraft ever built, the Airbus A380, which would be able to carry between 550 to 800 passengers on two decks. The six components of the airplane are to be produced in different European cities. They need to be transported from these cities to Toulouse, France, for assembly, and several means of transportation have been investigated. The sizes of the freights, the length of the itinerary, and the narrowness of the critical passages constitute a challenge that classical transportation techniques in the domain of oversized convoys cannot easily overcome. Therefore, Airbus and the French national agency in charge of road management launched a research and development project divided into two parts. The objective of the first part is to adapt functions first to develop for mobile robots to the complex kinematics of trailer-truck systems and integrating these functions into a software platform. The second part of the project aims to define and develop a computer-aided driving system on board the vehicles in order to help the drivers carry out their task. This paper provides a brief overview of the state of the art in trajectory planning for mobile robots and vehicles, and discusses the development of original solutions to address both the kinematic complexity of one of the vehicles and the need to optimize the distance to obstacles.     

Electrochemical polymerization of aniline investigated using on-line electrochemistry/electrospray mass spectrometry

A thin-layer electrochemical flow cell coupled on-line with electrospray mass spectrometry (EC/ES-MS) was used to investigate the soluble products from the controlled-potential anodic polymerization of aniline in H(2)O and H(2)O/CH(3)OH (1/1 v/v) with ammonium acetate and acetic acid or ammonium hydroxide as electrolytes (pH 4, 6.5, or 9). At a working electrode (glassy carbon) potential of 1.0 V versus Ag/AgCl, singly protonated aniline oligomers containing as many as 10 aniline units (10-mer) were observed in the ES mass spectra when the polymerization in H(2)O/CH(3)OH at pH 4 was carried out. The abundance of the higher n-mers decreased at higher solution pH and in 100% H(2)O at pH 4. Most of the oligomers were observed in more than one redox state ranging from fully oxidized (all imine nitrogens) to fully reduced (all amine nitrogens). The number of different redox states observed for the n-mers increased with increasing n. The structures of the reduced (m/z 185) and oxidized (m/z 183) aniline dimer ions (head-to-tail, tail-to-tail, or head-to-head) produced from the polymerization of aniline at pH 4, 6.5, and 9 in H(2)O/CH(3)OH were revealed to vary as a function of pH by comparison of their tandem mass spectrometry product ion spectra with the product ion spectra of the dimer standards. EC/ES-MS potential scan experiments, in which the working electrode current and major n-mer ions for n = 2, 3, and 4 were monitored as a function of electrode potential, were used to probe the growth mechanism to higher aniline oligomers. Under the conditions used, the controlled-current electrolytic process inherent to the operation of the ES ion source did not significantly influence the formation or nature of the oligomers observed. Beyond the current application, the results presented here serve to demonstrate the utility of EC/ES-MS as a tool in identifying the initial products of electropolymerization and in studying the products of electrode reactions in general.     

Intelligent Collectives: Theory,Applications, and Research Challenges

ABSTRACT

Nowadays, along with the rapid development of information technologies, very often solving a common problem is entrusted to many autonomous units (people, systems). With such an approach, one can tap into the so-called collective intelligence (CI)—emerging from the collaboration and competition of many individuals. In this paper, we present recent research on CI related to the effectiveness of using the wisdom of crowds to perform a wide range of problems. For this aim, we first introduce a general framework of CI involving key characteristics of intelligent collectives. Next, we focus on the problem of how diversity and collective cardinality influence collective performance. Then, its applications, which are widely used such as prediction markets and Delphi method, will be presented. Furthermore, some research challenges on the capacity of combining CI with other research fields such as machine learning and social networks are also discussed.     

Compensatability and optimal compensation of systems with white parameters in the delta domain

Using the delta operator, the strengthened discrete-time optimal projection equations for optimal reduced-order compensation of systems with white stochastic parameters are formulated in the delta domain. The delta domain unifies discrete time and continuous time. Moreover, when formulated in this domain, the efficiency and numerical conditioning of algorithms improves when the sampling rate is high. Exploiting the unification, important theoretical results, algorithms and compensatability tests concerning finite and infinite horizon optimal compensation of systems with white stochastic parameters are carried over from discrete time to continuous time. Among others, we consider the finite-horizon time-varying compensation problem for systems with white stochastic parameters and the property mean-square compensatability (ms-compensatability) that determines whether a system with white stochastic parameters can be stabilised by means of a compensator. In continuous time, both of these appear to be new. This also holds for the associated numerical algorithms and tests to verify ms-compensatability. They are illustrated with three numerical examples that reveal several interesting theoretical and numerical issues. A fourth example illustrates the improvement of both the efficiency and numerical conditioning of the algorithms. This is of vital practical importance for digital control system design when the sampling rate is high.     

Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas

An effective removal of atmospheric and topographic effects on remote-sensing imagery is an essential preprocessing step for mapping land cover accurately in mountain areas. Various techniques that remove these effects have been proposed and consist of specific combinations of an atmospheric and a topographic correction (TC) method. However, it is possible to generate a wide range of new combined correction methods by applying alternative combinations of atmospheric and TC methods. At present, a systematic overview of the statistical performance and data input requirement of preprocessing techniques is missing. In order to assess the individual and combined impacts of atmospheric and TC methods, 15 permutations of two atmospheric and/or four TC methods were evaluated statistically and compared to the uncorrected imagery. Furthermore, results of the integrated ATCOR3 method were included. This evaluation was performed in a study area in the Romanian Carpathian mountains. Results showed that the combination of a transmittance-based atmospheric correction (AC), which corrects the effects of Rayleigh scattering and water-vapour absorption, and a pixel-based C or Minnaert TC, which account for diffuse sky irradiance, reduced the image distortions most efficiently. Overall results indicated that TC had a larger impact than AC and there was a trade-off between the statistical performance of preprocessing techniques and their data requirement. However, the normalized difference vegetation index analysis indicated that atmospheric methods resulted in a larger impact on the spectral information in bands 3 and 4.     

Nonlinear optical beam propagation for optical limiting

We implement numerical modeling of high-energy laser-pulse propagation through bulk nonlinear optical materials using focused beams. An executable program with a graphical user interface is made available to researchers for modeling the propagation of beams through materials much thicker than the diffraction length (up to 10(3) times longer). Ultrafast nonlinearities of the bound-electronic Kerr effect and two-photon absorption as well as time-dependent excited-state and thermal nonlinearities are taken into account. The hydrodynamic equations describing the rarefaction of the medium that is due to heating are solved to determine thermal index changes for nanosecond laser pulses. We also show how this effect can be simplified in some cases by an approximation that assumes instantaneous expansion (so-called thermal lensing approximation). Comparisons of numerical results with several Z-scan, optical limiting and beam distortion experiments are presented. Possible application to optimization of a passive optical limiter design is discussed.     

A functional processor for the relational algebra on a microcomputer

We have built on the U.C.S.D. P-system (running on an IBM Personal Computer) a relational algebra processor, MRDS/FS, which is extremely powerful and which supports a functional syntax for the programmer-user. The relational algebra is provided in the extended operators μ-join, σ-join, project and select. The domain algebra is fully implemented for the first time, giving operations on attributes: arithmetic, logic, comparison and four different categories of aggregation of these. A strictly functional syntax is provided, permitting user-defined functions using the relational and domain algebras as primitive operations. An interactive editor permits the creation, copying and changing of both relations and user-defined functions.     

Robust H ∞ filtering for a class of non-linear systems with state delay and parameter uncertainty

This paper deals with the problem of robust H X filtering for a class of state-delayed non-linear systems with normbounded parameter uncertainty appearing in all the matrices of the linear part of the system model. The non-linearities are assumed to satisfy the global Lipschitz conditions and appear in both the state and measured output equations. Attention is focused on the design of a non-linear filter which ensures both the robust stability and a prescribed H X performance of the filtering error dynamics for all admissible uncertainties. A sufficient condition for the existence of such a filter is given in terms of a linear matrix inequality (LMI). When this LMI is feasible, the expression of a desired H X filter is also presented. A numerical example is provided to demonstrate the applicability of the proposed approach.