40 resultados para self-adaption two-control stabilization


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This paper compares and contrasts, for the first time, one- and two-component gelation systems that are direct structural analogues and draws conclusions about the molecular recognition pathways that underpin fibrillar self-assembly. The new one-component systems comprise L-lysine-based dendritic headgroups covalently connected to an aliphatic diamine spacer chain via an amide bond, One-component gelators with different generations of headgroup (from first to third generation) and different length spacer chains are reported. The self-assembly of these dendrimers in toluene was elucidated using thermal measurements, circular dichroism (CD) and NMR spectroscopies, scanning electron microscopy (SEM), and small-angle X-ray scattering (SAXS). The observations are compared with previous results for the analogous two-component gelation system in which the dendritic headgroups are bound to the aliphatic spacer chain noncovalently via acid-amine interactions. The one-component system is inherently a more effective gelator, partly as a consequence of the additional covalent amide groups that provide a new hydrogen bonding molecular recognition pathway, whereas the two-component analogue relies solely on intermolecular hydrogen bond interactions between the chiral dendritic headgroups. Furthermore, because these amide groups are important in the assembly process for the one-component system, the chiral information preset in the dendritic headgroups is not always transcribed into the nanoscale assembly, whereas for the two-component system, fiber formation is always accompanied by chiral ordering because the molecular recognition pathway is completely dependent on hydrogen bond interactions between well-organized chiral dendritic headgroups.

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In order to ease control, the links between actuators and robotic limbs are generally made to be as stiff as possible. This is in contrast to natural limbs, where compliance is present. Springs have been added to the drive train between the actuator and load to imitate this natural compliance. The majority of these springs have been in series between the actuator and load. However, a more biologically inspired approach is taken, here springs have been used in parallel to oppose each other. The paper will describe the application of parallel extension springs in a robot arm in order to give it compliance. Advantages and disadvantages of this application are discussed along with various control strategies.

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In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

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It is common to make the links between actuators and robotic limbs as stiff as possible, in complete contrast to natural systems, where compliance is present. In the past, to create some compliance in a drive, springs have been added to the link between the actuator and load. Many of these springs have been in series with the drive, but recently a more 'biological' approach has been taken where two springs have been used in parallel to counteract each other. This paper describes the application of parallel extension springs in a robot arm in order to give it compliance. Advantages and disadvantages of this application are discussed, along with various control strategies.

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The self-assembly and hydrogelation properties of two Fmoc-tripeptides [Fmoc = N-(fluorenyl-9-methoxycarbonyl)] are investigated, in borate buffer and other basic solutions. A remarkable difference in self-assembly properties is observed comparing Fmoc-VLK(Boc) with Fmoc-K(Boc)LV, both containing K protected by N(epsilon)-tert-butyloxycarbonate (Boc). In borate buffer, the former peptide forms highly anisotropic fibrils which show local alignment, and the hydrogels show flow-aligning properties. In contrast, Fmoc-K(Boc)LV forms highly branched fibrils that produce isotropic hydrogels with a much higher modulus (G' > 10(4) Pa), and lower concentration for hydrogel formation. The distinct self-assembled structures are ascribed to conformational differences, as revealed by secondary structure probes (CD, FTIR, Raman spectroscopy) and X-ray diffraction. Fmoc-VLK(Boc) forms well-defined beta-sheets with a cross-beta X-ray diffraction pattern, whereas Fmoc-KLV(Boc) forms unoriented assemblies with multiple stacked sheets. Interchange of the K and V residues when inverting the tripeptide sequence thus leads to substantial differences in self-assembled structures, suggesting a promising approach to control hydrogel properties.

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We study the effects of NaCl on the self-assembly of AAKLVFF and beta A beta AKLVFF in solution. Both AAKLVFF and beta A beta AKLVFF self-assemble into twisted fibers in aqueous solution. The addition of NaCl to aqueous solutions of AAKLVFF produces large crystal-like nanotapes which eventually precipitate. In contrast, highly twisted fibrils were observed for beta A beta AKLVFF solutions at low salt concentration, while a coexistence of highly twisted fibers and nanotubes was observed for beta A beta AKLVFF at high salt concentration. The self-assembled structures observed for beta A beta AKLVFF in NaCl solutions were ascribed to the progressive screening of the beta A beta AKLVFF surface charge caused by the addition of salt.

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A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

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In the last few years a state-space formulation has been introduced into self-tuning control. This has not only allowed for a wider choice of possible control actions, but has also provided an insight into the theory underlying—and hidden by—that used in the polynomial description. This paper considers many of the self-tuning algorithms, both state-space and polynomial, presently in use, and by starting from first principles develops the observers which are, effectively, used in each case. At any specific time instant the state estimator can be regarded as taking one of two forms. In the first case the most recently available output measurement is excluded, and here an optimal and conditionally stable observer is obtained. In the second case the present output signal is included, and here it is shown that although the observer is once again conditionally stable, it is no longer optimal. This result is of significance, as many of the popular self-tuning controllers lie in the second, rather than first, category.

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Variations on the standard Kohonen feature map can enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a hybrid learning system (HLS) which has arisen out of a genetic-based classifier system. In the paper a description of the modified feature map is given, which constitutes the HLSs long term memory, and results in the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.

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The self-assembly of tripeptides based on the RGD cell adhesion motif is investigated. Two tripeptides containing the Fmoc [N-(fluorenyl)-9-methoxycarbonyl] aromatic unit were synthesized, Fmoc-RGD and a control peptide containing a scrambled sequence, Fmoc-GRD. The Fmoc is used to control selfassembly via aromatic stacking interactions. The self-assembly and hydrogelation properties of the two Fmoc-tripeptides are compared. Both form well defined amyloid fibrils (as shown by cryo-TEM and SAXS) with b-sheet features in their circular dichroism and FTIR spectra. Both peptides form selfsupporting hydrogels, the dynamic shear modulus of which was measured. Preliminary cell culture experiments reveal that Fmoc-RGD can be used as a support for bovine fibroblasts, but not Fmoc- GRD, consistent with the incorporation of the cell adhesion motif in the former peptide.

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A three degrees of freedom industrial robot is controlled by applying PID self-tuning (PID/ST) controllers. This control is considered as a corrective term to a nominal value, centrally computed from an inaccurate and/ or simplified dynamic model. An identification scheme on an assumed linear plant describing the deviation from the desired trajectory is employed in order to tune the controller coefficients and thus accomplish a behaviour prescribed through a desired pole placement. A salient feature of our approach is the decentralized nature of the controllers producing the corrective term for each joint. This opens the way to practical implementation, as recent computing requirement calculations for similar set-ups have shown in the literature. Numerical results are presented.

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An external input signal is incorporated into a self-tuning controller which, although it is based on a CARMA system model, employs a state-space framework for control law calculations. Steady-state set point following can then be accomplished even when only a recursive least squares parameter estimation scheme is used, despite the fact that the disturbance affecting the system may well be coloured.